{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking convergence in DisMod-MR\n",
    "\n",
    "This notebook provides some examples of running multiple chains and checking convergence in DisMod-MR.  Checking convergence is an important part of MCMC estimation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np, pandas as pd, dismod_mr, pymc as pm, matplotlib.pyplot as plt, seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set a random seed to ensure reproducible simulation results\n",
    "np.random.seed(123456)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# simulate data\n",
    "n = 20\n",
    "\n",
    "data = dict(age=np.random.randint(0, 10, size=n)*10,\n",
    "            year=np.random.randint(1990, 2010, size=n))\n",
    "data = pd.DataFrame(data)\n",
    "data['value'] = (.1 + .001 * data.age) + np.random.normal(0., .01, size=n)\n",
    "\n",
    "data['data_type'] = 'p'\n",
    "\n",
    "data['age_start'] = data.age\n",
    "data['age_end'] = data.age+10\n",
    "\n",
    "# for prettier display, include jittered age near midpoint of age interval\n",
    "data['jittered_age'] = .5*(data.age_start + data.age_end) + np.random.normal(size=n)\n",
    "\n",
    "# keep things simple, no spatial random effects, no sex effect\n",
    "data['area'] = 'all'\n",
    "data['sex'] = 'total'\n",
    "\n",
    "# quantification of uncertainty that says these numbers are believed to be quite precise\n",
    "data['standard_error'] = -99\n",
    "data['upper_ci'] = np.nan\n",
    "data['lower_ci'] = np.nan\n",
    "data['effective_sample_size'] = 1.e8\n",
    "\n",
    "\n",
    "def new_model(data):\n",
    "    # build the dismod_mr model\n",
    "    dm = dismod_mr.data.ModelData()\n",
    "\n",
    "    # set simple model parameters, for decent, fast computation\n",
    "    dm.set_knots('p', [0,100])\n",
    "    dm.set_level_bounds('p', lower=0, upper=1)\n",
    "    dm.set_level_value('p', age_before=0, age_after=100, value=0)\n",
    "    dm.set_heterogeneity('p', value='Slightly')\n",
    "    dm.set_effect_prior('p', cov='x_sex', value=dict(dist='Constant', mu=0))\n",
    "    \n",
    "    # copy data into model \n",
    "    dm.input_data = data.copy()\n",
    "    \n",
    "    return dm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fit the model with too few iterations of MCMC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using stored FE for beta_p_x_sex x_sex {'dist': 'Constant', 'mu': 0}\n",
      "finding initial values\n",
      ". . . \n",
      "finding MAP estimate\n",
      "\n",
      "finding step covariances estimate\n",
      "\n",
      "resetting initial values (1)\n",
      ". . . \n",
      "resetting initial values (2)\n",
      "\n",
      "mare: 0.04\n",
      "sampling from posterior\n",
      "\n",
      "CPU times: user 5 s, sys: 24 ms, total: 5.03 s\n",
      "Wall time: 5.03 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dm1 = new_model(data)\n",
    "dm1.setup_model('p', rate_model='neg_binom')\n",
    "%time dm1.fit(how='mcmc', iter=10, burn=0, thin=1)\n",
    "dm1.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fitting it again gives a different answer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using stored FE for beta_p_x_sex x_sex {'dist': 'Constant', 'mu': 0}\n",
      "finding initial values\n",
      ". . . \n",
      "finding MAP estimate\n",
      "\n",
      "finding step covariances estimate\n",
      "\n",
      "resetting initial values (1)\n",
      ". . . \n",
      "resetting initial values (2)\n",
      "\n",
      "mare: 0.04\n",
      "sampling from posterior\n",
      "\n",
      "CPU times: user 5.22 s, sys: 16 ms, total: 5.23 s\n",
      "Wall time: 5.24 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dm2 = new_model(data)\n",
    "dm2.setup_model('p', rate_model='neg_binom')\n",
    "%time dm2.fit(how='mcmc', iter=10, burn=0, thin=1)\n",
    "dm2.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.6655739332611623"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm1.vars['p']['gamma'][1].trace().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.6456277627674403"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm2.vars['p']['gamma'][1].trace().mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fit with more MCMC iterations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using stored FE for beta_p_x_sex x_sex {'dist': 'Constant', 'mu': 0}\n",
      "finding initial values\n",
      ". . . \n",
      "finding MAP estimate\n",
      "\n",
      "finding step covariances estimate\n",
      "\n",
      "resetting initial values (1)\n",
      ". . . \n",
      "resetting initial values (2)\n",
      "\n",
      "mare: 0.04\n",
      "sampling from posterior\n",
      "\n",
      "CPU times: user 1min 17s, sys: 360 ms, total: 1min 17s\n",
      "Wall time: 1min 17s\n"
     ]
    }
   ],
   "source": [
    "dm1 = new_model(data)\n",
    "dm1.setup_model('p', rate_model='neg_binom')\n",
    "%time dm1.fit(how='mcmc', iter=10_000, burn=5_000, thin=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using stored FE for beta_p_x_sex x_sex {'dist': 'Constant', 'mu': 0}\n",
      "finding initial values\n",
      ". . . \n",
      "finding MAP estimate\n",
      "\n",
      "finding step covariances estimate\n",
      "\n",
      "resetting initial values (1)\n",
      ". . . \n",
      "resetting initial values (2)\n",
      "\n",
      "mare: 0.04\n",
      "sampling from posterior\n",
      "\n",
      "CPU times: user 1min 17s, sys: 24 ms, total: 1min 17s\n",
      "Wall time: 1min 17s\n"
     ]
    }
   ],
   "source": [
    "dm2 = new_model(data)\n",
    "dm2.setup_model('p', rate_model='neg_binom')\n",
    "%time dm2.fit(how='mcmc', iter=10_000, burn=5_000, thin=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.651725841034799"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm1.vars['p']['gamma'][1].trace().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.650470474364036"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm2.vars['p']['gamma'][1].trace().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CXf/KQgDAgL49cPGJh+JbA/bF7qY2tLUzhh64b0e4xua2tDxq93Su/vzijbg55Bu/OEvJvxYz1u1ICr+rsQX99umOrl0oyfTy1AenovSRyzq+q60yvyneiZLqRpQoq4JnH3EgTh6yH4BOBSnGjEy60hcryzF+hf4Km+A9v3wrvqKoftdC8Hy5qhy3vb8cz11/GkadOihocQTBPLk08hIEIatxRbnyG602+NqXF+DPFx6Ds484MO3eUffE9zn9aeTR+NPIYzr2aMVijN3NbZhXtBMjjjsY9U2t6NG1C2Iq7c3Mni0zyp56JlmtWL38dQnOGLo/zj1a35FHIuYRA3pnzghAdUMLrhgzD5ecdAjyN+9CVUMzNv2n4xgknHj/lLQ4j09Z3/F51vpkRx6NKQ4sJhdW4u5xq3Ht8MPw6NWnoLVd/wGof3eq0tQWi4GZQURQLVx1rmLp8AeDfWGCkMsUbY+vJhbvMLeqKAiCIAh+kEtGRq6bBfpBTEObWbSpxtBZAwAsKa1J+p7YA5VI75QHpuLS5+YmpZPQrZ6auh5vLyjF5IIK5I2eaErON+eXAogrU9e+vFA33BNT14NNTttVa3hbmVxQgf/OKOr4/r/8rQCArwoqO8zqFm2qSYunZuqa7WnXEorRnpbkla6EF8XPO8wf9fdVxRj474wirCmvT1vhm7OhGoffNQmrymo7FCpmYzO/NpPeBwUhF0m0Iu53YrnULQqBIEVMELKaXFqcjubKlc03VNOY2RSvpKoxabUqoWA8p7P3ygzMwGKVYte9KyWt9qhXakijh1Gv/vzlo2QFsqUtht++q+1hUM22XXsN72vtbXp2RhH+NPKYjKt3Ru7WGYwnp23AMzOKkkwjAWDGurhCt3hTDQb27QlAW3FOsGJrLX7k0GOiIGQzxTsakr6XKia4eSZXvQUhMHJp5CUIOUQu7ouP5MrVhu0NmQNpsLaiHusq0928pg7oN+/ck3TP7c3hlFLShuzfuR9qr4YzCnXuy1JcIT87I92xhVt8nB/3/qf385vbYrjx1UXYUa+/Ly0Rtz3GaE4540udbuKZMNKfT4I3v9lkTnBByFEmra5M+n7+E7Nx/hOzgxFGEGyQg+MwQchqctG/UiSVqxlrd2QOpIPWXgSj1ZJ2Bi7/7zzd+3ZIVdbaY7Gkwtfcpu/tT6131De1olSlCBrmaUnChByZTfDmFVejaIe+sqte9Up1pJF47kmPg4EuOr1r1y6RLK6C4Du5OFMoZAc5OA4TBCHLiKRZoJOBg7bZnX54ZjZ9EK5ZUq3s2jKY3SXkW1eZrMSc8sBU03naWX1rVlbRMsVMTVqdl1pxTQ+XHkfLW+Dv3luKIfvvm7b3K4HeSpcgZDtF2xuwsqwOV58xJGhRBEEQBCGNXByiRVO50rletmsvmlrbDV/k799P358UY6ChSXs/lhWX6WZJXSlrj2VyZ+F8Ls9OCu2KSeQHi7dkSDs59Zkqt+1JClRKuI6VKyQrYKnKUqqpkxAeFmzciXOOTPfQKfjDhU/PAYA05UprEskZsp4g+EMOjsMEITAmrCzHhccfnHbmquCMSNpZLUnZd6Tm3s8LELPsUI6xYmut5p2axhbN605IXcFpi2nv66qsa8L6Snv7y9IztR6FAFTUNWGpwfMGgJnrkl23J85AAsyvXHVcA+fkLEdUSZytlssUbW/Abo2z44JE6pAgCIJgxJLSGvzxg+V46Ms1vuSXS4fbR1K5SrgC1+LjpWWm3ZonMHrfWgfuuk3qylVCnp+9uhAXPTPHlc2AVp8JEPdi2GJi39UXK/UP9NXyvJhgvcoxSeJWjIGuepuuBCGEXPj0HNz8+uKgxRAEQRAE0yQstirrjL1JC9aJpHKVCavKiJFDi8ZmfecSbhFfuer8vqO+GcyMkqq4G2U3dH1bChpl3g+WiaSVK4NwCeWPGehmUbkSVUwImvwMq7t+k0MThIKQRsG2Omwx6exJEAR/yKX98dmpXFkM/5HiclwLowNy3aI9FksS+rzHZ+HlOSWu5mFHRyIYK55GJA77Vee7YONOEzE56dwvQRCsY2elOpchomuIqJCIYkQ03CBcKRGtJqIVRJSvun4AEU0joiLl7/7+SC5ocfl/5+G8x2cFLYYghBq/J+HELDDinHT/lKBFsERbO6cNhr4pru74HJhZYBdCW7u9zBMu4tXKWZGGG3wg/vvUZoGCEHa21zfhxdkbc6qzyHIKAFwFYI6JsD9g5mHMrFbCRgOYwcxHA5ihfBcEQQg93q8o5d6EeVYqV1FjXWUDWlOUGLdXb+yMAZ2sXCUwqywlsmltj8mce8Roa4/hs2VlOaVo3Pb+Mjw6eR02bNeeMAiaHHoVrsDMa5l5vYMkrgTwlvL5LQA/ci5VdnDf+AL85X8rMoZzY7V1VVkt8kZPdJyOIOQS3vfdudchiXKl0B7wkklFyoZC9baj1dvqHKdva8sVkfM9Vybiq0PMLarWDSeEk5fnlOAvH63E5yu2BS2KbzQ0xR3dBN1u+E0u2czrwACmEtFSIrpVdf1gZq4AAOXvQVqRiehWIsonovyqKn3HTNnE2ws247Pl/rQNby/Y7Es+gpANSHPuHaJcKdz8xpJA80+dOFCvXL309Ub3MzABQdkP5gAzK19ryuuTFKxJqyss5SENRLBUNTQDAHY1un8mnNc0Nrfh7H/PwMISM/sBo0NuqXzmIKLpRFSg8e9KC8l8l5lPB3AJgN8T0XlWZGDmscw8nJmHDxw40JL82Y77Z7MJgnPa2mO485NV2FTdGLQoruOfhUPu1W1RrkJCahl3e4Z4gY3BI5H9PVcJzMzsT1hZnrQsvbrM+UqdIJhhTUU9Kuub8PgUexZhoXUcIXaBaTDzSGY+SePfeAtplCt/dwAYB+As5dZ2IjoUAJS/O7RTEPRwoy7l3hBO8JrV2+rwv/yt+JMJ09aoIhYJ7iPKVUhIXeHp6vKbmbS60lQ4dR1jZjwzvchRvmbHeGpzQKseGqVdEOySKJ9ShIC1FfWhOww5TBBRbyLqm/gM4IeIO8IAgAkAblI+3wTAtMImJCMDPUEQoo4oVwHxvaMGJH1P3VAYlDtytRgMoIvDEvL3T1eZCqc2BWz1wf294D5RHhN5Lfvu5jYs3VzjbSYqrK4BMDMueXYubgnYPDooiOjHRFQG4BwAE4loinJ9EBFNUoIdDGAeEa0EsBjARGaerNx7BMCFRFQE4ELluyAI2YJYAwgWEOUqIEYcl7zf2WjPVZCcPLi/L/moHWc0tVpTrur3ymy7YI/EpIbd/R7qelteuzftLLc15fUo2FaHP7y/DD95cQHq9oR7X9riUv8UwDDBzOOYeQgz92Tmg5n5IuV6OTNfqnwuYeZTlX8nMvO/VPF3MvMFzHy08jc3H6QL5JLXUQEgoouJaD0RFRNR2hEGRHQeES0jojYiulrjfj8i2kZEYzySz4tkc5Jcqtk5q1xdOWxQoPmn1tfUvUlSn82zpWZP0CIIHrN4Uw3WVdabCsvMOOPhaXh/0ZaMYTuqnQv1bcSTs3H9KwuTrl363Fxc/t95mLU+7hmuub3deUaCIGgi/Wa0IKKuAJ5H3EHMCQCuJ6ITUoJtAXAzgPd1knkYwNdeyWiX1vYYirY3BC1GKMjFepmzylXY3nWqchWWlavQbtgXQoMfM80/fXkBLn5mrunwOxtbcPe41RnDJcq3G7XN7IrrOws3pyl+q8vqsL7SvY64WOfAbj1ksUAIC7JSkFOcBaBYWRVuAfAh4mfGdcDMpcy8CkBaA0tEZyBurjvVD2Gt8K+Ja3Hh03NQtiu8k79+tfu52L/krHLlJn+76FhcO/wwR2m0pihX3boE38EEL4EQJbTKSxAmPpayTDi08KmwEwj3fl6QpvhdMWYeLnpmjmv5fFVgzoGNIESRZVt2obx2b9r1TOa9tXtakDd6ouXjPgTPGAxgq+p7mXItI0TUBcCTAP5mIqzv58vlK3tso3BEiYz13CdnlSs3Z8d+cOxBOOaQvtbyT/meep5U1xAoVwByy0hWcJ0gZqw6LP1MVKFOq0Dne66iTKafkS2/U8gOrnphPr7zyEzL8RIruq/N2+S2SII9NOfkTMb9HYBJzLw1U8AgzpeTNrOTXFyM7ha0AEHh5rt26lEPQNp5UmFRrqR9EDJhVEaCKD+dTirMhI3/9W3lKhzVWpewyycIbiBOM0JDGQC12c8QAOUm454D4Fwi+h2APgB6ENFuZk5ziuEGdktMFNpUqQ3uk7MrV25qV+TC2fLLt9YmfQ+LciUITgjELNBGHDc7wHHLy/TzcS8bT5Axp5DNJOq5FPPQsATA0UR0OBH1AHAd4mfGZYSZb2DmocycB+AOAG97oViFvc0WwknOKldVDc2updWFrA/OUs0SX5y9Mel7WJQrmeETzLKqrC7tWpClx4zpb6dDC5tmgRq/8P7xhQCAKYXR2fdkVM/nFVVj9bb0dysIbuJPVxOOflWIw8xtAG4DMAXAWgAfMXMhET1ERKMAgIjOVM6guwbAy0RU6KuMfmZmg41Vu1G8w5kzJKkV7pOzytXcomrX0iJyvnKVShiUqygsZwvh4bPl29KuBbLnykKeMQ/MAhlA2a49+M07S91L1Ce0nsONry3CDmUyamphJVaV1aYHEgSXcFIXzcYNes5w/IptqN6dPsHb2h7DU1PXo7E5d85uZOZJzHwMMx+ZODuOme9j5gnK5yXKGXS9mflAZj5RI403mfk2v2UPAxc8+TVGPuWeMyTBHXJWuXITL5SQsBw26nYndPyh/dxNUAg1MY0CtKqsFq3t1g6KtoKXxwc8MWU9lmQ6aJeBplbt86zCPguaiY+XlmHUmG+CFkNwCDNjY5U1d/1+4aXiE4YJw6qGZtz+4Qr8+u38tHvjlm3DczOL8dS0DQFIJmhht8gErcCbwW8Ro/BM3EKUKxewcyZVpihaqwD+435PdM0ZQ1xPU4gO6ysbMGrMN3hs8jrP8uhwUmEqrLXWfsysYlzz0oK0vMwSps6lvqkVeaMnYvyKbZqd7LItu8SrWpby2rxNuODJr7Fia3hWId1QfEyvXDnPyjaJiaXKuqa0e83KPb3JGUEQooEoVy4Q33MVgikxD3C7E/LqMcnesOAwevSp9xKmMIXl9bby2lpj/UDGdZX1yBs9EQUa+4YS4vl1aHeYDuXesjP+LF/+ukTz/lUvzMfDX67xUyTBBGf/ewaenV7kKI2EA6UtNuqTV/jRhIepl9b8vdKPhRYvX82b32wydei92/hdH4IeJje1tuOCJ2fjm2L3tgXpIcqVCxAIB/bpEbQYnhCVtr49FhFBI0gsxnhrfima26zPpqYqE07L07mPzbIcZ1rhdgDAVwUaB4c63HOlOT6yGkEQLFBZ34SnpzszG0sU9zBOSvkyAGPGyq21ePMb/1dmgx5gCtbw43098MUavL9oi/cZBUzQzc2Wmj3YWNWIByZ47xNFlCsXIAIuOvEQa3E8kiVXEd3KOz5fsQ33TyjEf2cUW46r15jO37gzyeTsqWkbcNUL2nt5SizuDdE7u0rL7Yxa+Xtq6nqc9tBUg3TNFTKjcGEspozgOz3BOTvqm9DS5t1eRr9wVhaNe1a1hcmVz3+DB74Iz8rs+soGbN21N2gxhBSyuW3066fl4nhXlCsX6NKF0L1rdj5Kt82YvGqowmRulW3sVjxX1e3VdrJi9OyN3ora5Oy5GUVYtkV7/8eeFmsrZqnu1Q0POVbtz3puZjF2GTiSMVt2G1vadb03LSzZaS4RH5AZ9OyhpS2Gs/49A3//ZGXQokSCMPQWqe3mRc/Mwdg5cRNdqZvRJwxlzCxel7coPQu3yE6NwGeytR30osJ5VcmyeXYpARE9TkTriGgVEY0jov4aYQ4jollEtJaICono9iBkTaDlLdAsjc1tqGlscSyD3kpW8r3Mhb1d9VvWVdo7V+T2D1fYiicIRrTF4itWUxQT2EyEeY+wl6KF4Ve7f3CL4CVOy2OIq1oHuTB+8htRrlzAr83wflPV0Ox6pfPKxj9HGodpAE5i5lMAbABwl0aYNgB/ZebjAZwN4PdEdIKPMibh5L2MeHI2Tn94mqU4b3yzCa/N1d5HoVVL2eBeKlr7+sK4Z8UOsvKbHZh9j6nBMRAAACAASURBVEH2WBu2N+Ck+6egoi44EzitA8/dYE9LG95eUJo17YIguEF2jpCNEeXKBWyd95ulCllQOFkhiQrMPFU50R4AFgJI82vPzBXMvEz53ID4qfeDvZTLcKDCqV/Nv6ft9emHbGbiwS/W4MnEGTGUOc/dzXEzQDPVUauIZVupk1YpmkRpNeSdBZuxu7kN09aYW2WzQqZ67HW3+59J63Df+ELMWLsjY9gc6LKyimyegJLhqPuIcuUGdgqmtKyukgvKVQq3APjKKAAR5QE4DcAinfu3ElE+EeVXVVVlzJDB+PektSjekexgwki5CrpDam2P4ZmE22pVD7KnpQ1ba/bgz/9L7FGxZhaYTTCzNEchZ1VZreZRAnZQH+Cd6b3HYoyYR96CgihzZpXQxyavw0dLtlpOv2ZP3Ix5r845VUs370JVg7lJoxdnb8QfP1huWQZBEIKnm9MEiKgrgHwA25j58pR7fwHwK8RNlaoA3MLMm53mGTbsmAXOXp95MBsGdjSkH3ToBO8cWmQHRDQdgJbryXuYebwS5h7E69R7Bun0AfApgD8xs+ahUsw8FsBYABg+fLjmI2RmLNu8CwBQXtuEmet2YNJqDZfmOqS+bz9n2AlArY6DiptfX4LFpTWdYU2IpaXAi1Ii+MGoMXFPmqWPXKZ5PzGJkak81u1txakPdnrEzDT5cfVL87FsS61uvnaIwiz5C7M3AgB+euZhmvdPfmAKrjnjMNx3hTWL65+8ON902EeVg9afu/40S3kI3mC17xLT0PDix5txY+XqdsRNj7RYDmC4skfkEwCPuZBf6LCjXOnNbIWNSasrgxbBFBx9D8QAAGYeycwnafxLKFY3AbgcwA2s03oTUXfEFav3mPkzJ/K8u3AzPl9RniKjcZy9Ku9+qUG1BnNrMhwobLePSnfFHmdHQ1OSYqW+Z4RXM/ipLCrZiTEz0w+J3bm7GbV77Dn4eHtBqZwFl8WYrSOJQ7zNoufB0w28GHz6pbc1NLXhdYdnZFn59Wsr6nHk3ZOwrVZctQeFXSuMMJvsigLoHY6UKyIaAuAyAK9q3WfmWcycOAJec49INiBbrszjlpnYZScf6km6YYaILgZwJ4BRqnqVGoYAvAZgLTM/5TTPoh3pZ0wZNcjjV2zD8fdNxobtcW96mcw1mRmXPjfXmZAWmLiqAmf9a0badXMrVx4IpMG1Yxfiianph8Se8c/pGPaQNQcfCe4bX4iP8pPNnMLc6fsNEV2jeNeMEdFwg3ClRLSaiFYQUb7q+gNEtE25voKILvVH8jhmi2bqGw/GNE8bP0Txu99dtmUXfvHGYrS1O5v9e3/RFrTHGDPWur9PTTBG2kk3Ccc4zY836nTl6hkAfwdgpuX4JTLsEYkqdlaupMI648TB/ZK+58ik/BgAfQFMUwZwLwEAEQ0ioklKmO8C+DmAEW4P9MyU2OnKRu61FfHVqNTBW+r3z5Ztc0EycxABS1JWrKygvfITjoL3+JR1KK1uxOD++3Rc21qTrH/vbmpLjSZ0UgDgKgDaB5Ql8wNmHsbMqUrY08r1Ycw8STOmR8gMdLhIvI3bP1yOWeurNFecrLyyXJg8FPwnzEcyeIkftcn2nisiuhzADmZeSkTnZwh7I4DhAL6vc/9WALcCwNChQw3SCeceB1JU1H69uqHe5AAmR8u0a+8vfdAewoLhMsx8lM71cgCXKp/nwcWJGase8hKeMxPxMg0Kdpjc3G0HApkelJiZIHG7jM0vrsZ3jhrgSlrPz9qIrwoqMbj/Ph0DuXMfm5UURgZo+jDzWiC6g40oTi4x4nUq8cyj+eSN6TyknLB0s/mJHb3J12x8RlEhG4cY/o+bgi3BfubuZOXquwBGEVEpgA8Rnyl/NzUQEY0EcA/ipkyaIylmHsvMw5l5+MCBA3UzDGvDkpBr6b0XBipHJq4+I3irTLeqcmqjEMXBRdQwM+6cW1QNQDWQT3VoEWAlNlotbjKxB9LtIvazVzWdONqmrd1YwukG7qGzceDgEQxgKhEtVSYF1dymHPD9OhHt77tUJkhVHoN87w9+sQaH39W5wOeGKGHTjdUHmP/kxQWaYWoaW9DSlmz8o54I2dXYIvVT8Bi/Kk6wBdnP3G0rV8x8FzMPYeY8ANcBmMnMN6rDENFpAF5GXLHKfPBDBtycVRzYt6draSVmvbt3Nf84g5gh7deru+95puLZypXMynuC1eda0xh3uMDaupWt9+/k3Zo1v52l8t65YONO0+l7Oeip3t2Mdxead66aqUlZvKkmbRCXSxDRdCIq0Ph3pYVkvsvMpwO4BPEDus9Trr8I4EgAwwBUAHhSRwZLxx+YxWwdmVdcnRLPfzL1fVZ6RqtOWvzqdptb2004wInfP/3hafjNO/m6od5QO84Im/aYQ9h99PLKwvcMorDnKg0ieoiIRilfHwfQB8DHyt6PCY7SdixdJ+/+8tuupWVvz5U+3buGrCS6iFtKUGq/JTN73pD8XEnjmnG8TGHdKg+7m9vSzo8hspf+GzpewPwoY/mqPWG/e28Z/vF5AUqq0p2K6JHp96odjIStw/OaTJ44TaZRrvzdAWAcgLOU79uZuZ2ZYwBeSVzXiG/KSsMqepMZqdz7eYFreXqFlWo2tyicR5r87ZNV+Punqzq+Z6prs1KOZkmdFJLuLXhkjJE9hHrPlRpmng1gtvL5PtX1kW6kn2DoAfuipLrRlbR6dnNPr3R7kBJvWD1wU5tFg6nUQaQ0fN5jpfxwx9/OF9Nq02PW7ubM+xgvenpO2qbxVHHjezbtFxQ/VkevfqnTdCixCthmcnbeSfWWld/MEFFvAF2YuUH5/EMADyn3DmXmxAFwP0bcQYZv5OrbC/IcvUx8srQMg/brBcCZpYr6J4bn1+UOdl+djEnCR1T2XPmPi0/GTUXDTlpGcbxSgsLQMLvV4KSONzO5/Bbsoe0fL/OzTrwP9Xs6+h57zkJ/9Vayycz6yoa0MEGd/+JlqUsoglr1dmphJYY9NFXjjjOi6tDBKUT0YyIqA3AOgIlENEW5rvbEeTCAeUS0EsBiABOZebJy7zHFRfsqAD8A8Gc35JpXVG3KjbfdSQMnkw1fra5IWzliZmzZqXlKhGm8LIFWla/2GDuckEnkq3FPuixB8JVI7LkKAjcbXTdnuNw2C/SKVHv7SJPSM0k/5T12yqydgUlheR12qg473dOS7GyivM6cItXSHsP2us50nNY5Tc+JDgvego07cf7jswzDaDUv/560FrV7WlPCkb09bSwDPWYep+wh7snMBzPzRcr1cmZOeOIsYeZTlX8nMvO/VPF/zswnM/MpzDxKtYplm/kbq3Hja4vw3Iz0A6XT5Ff+trTFcMw9X6W54feC/3tvGX7+2uKka58sLcN5j8/CwhLz+xbdJFNXbLWrPvLuSXj9m1Lb8qgdWjhJI9frp5Ad5FI5jpRy5SZuTtDaUa6M4ng1ebxOY8Y/qvzotMFJ3zNvHhbsYFuh0NlzlRp1XUV6mbzsuXm45Fn9g4W7d4k3WxurduOSZ+eiLkXJSNDazrhizDwTwhrLmOm6Ex7+cg1Kbcz0u7HC5CSJjRb2gQn2SOwhfHfRloxh1fWspT2GyQWVmFxQgZVba43jOZIwnRVKflqHjyewWu7eXlDasUI2f2O1ZScWLW2xjv7BTpn/dGmZ9UgKiVV+J5O5akuBHF1YjjRhfme5NmrSexXb65vw3iLzzqPMIMqVG2m5nH+Y7Mbdxo1zFRbcNQJHDOzjgjSCFaztuTL3niesLNe8bnT+VTfF4cuYmcVYW1GPGeu2m8pr1bY6TFxdaSqsXxg9Uzs1JVOc4+6d3PFZqyq2xxj5pTUZTdEuePJrG9IJdkjsvTMitb5tr2/Cb99dhiuf/yZTRE8oV8x0tdp7q/3bfeML8fPXFmN+cTV+9soiPD+r2HTciasqcMw/vsJDX66xlKdbGHV3Zh99PI1cGwaHj1xadfGKsCqav3orH/eMK0CFSasYM+SwcuXeW7aXVEhLWQTQ6pxlz5VX2N3LkfzXTbp1SX7/D0woNBVv2prtqN5t/9Bivw9cLKlKOO8x11ZscuDsR/3LPllahnvHh9+rnKAipWjaWQ11U4wXZ29Mu5c3eiL+8flq22knJlyKDVbFUkmU4zfnl+qG8XIVNvE8uljs7vXGFNk88ZptRMFJkN+lKehhml72Hc6jMpwVaYVIKVduKUTHHdLX3f1bdvZcSRvpKkFX2lxib0vmA3cXKy7FvehgVpbVxdNWXnp9U2ZvgnZZunmX4f4RKwO9sGHUBo1fob2iKASL3v68sDZ/qe3yuwszmzjq0UXRUNQTaWsq6g3jpK76aSknFzz5NS57Tt8M2Qkdolrs79XPjSH9m+ANfhWrXJwUiJRy5RZ9e3ULXLkxyj5o2YKkd4+uGcNoPR9ZufIG9WNNNJANJlyjf7ZsG4B0r45u8LCPJj4/eXE+rhu7EMyMJ6duSLt/9zj7M/GAubquFUYvmp3VNS0FuE9PV07pEBygNWmntyKV+tqnrzVnKhum2fVMZTex+qNu6x+fsj4pTKbqpFffCsuNlTT76P8mv1fCBWdk89vyeswZlnZGXLH7gB0nFG6SqwpUplW+A/v01LyeabDXWwaDnvDhkq2O4qcOIDJtsA8r6yobMG75tkDyrm5oxoKNnatneaMnunbeX4Lm1uTVyFxtn8LCqrJarK/MPOCfWliJccvLbA9eghjfZypb6rPe1CT67FjKdsDiHbuxfMsuN0RzncTzvfHVRZbiqZ+R6GDRpMNTZBav2tTtaXVkap/N5OSI9L7LTwy8uBtVOKuyPX3tqfjz/1Y6E8gnzM5OpnLkwN4d5mBaHNyvlxOxBBPYGcClxnhqWvrqj138HHP84o0lPuaWzLVjF3qexz8+lz1WYWLUmAyOKBRufWcpAGD+6BFeigNmtmX+bqWOZkpfa+UKAEY+FXeuUvrIZaE7py0h6Ybt2ubDZlavGOyKS3fBGfLo0zlVOWux9JHLDMO5rWDuaGhCVUMzThy0n6vpuknOKVc/HT4EJw/ZDzsamoIWRRerHUQ2zYyY+e3Z82ujhR0Tv2yZda2sD297ocbSYFapSZt37kkb/GVTm5ILeF3NmK0P7Pe0tKFH13TjGLsmrR0rVw5+rBeletLqCuy/bw8ce0hfVKQcZm70m0zvpUoyzRaCYk1FPVrbY+iuUaYFf/nB47PR2NKeUakLkkgpV240LIlBQ9CDhy4u1k+zh6pGAZmZyzasjYR+9Va++ZSzQHELuh0CgOY2Y7frQvixe86faXfgKd/HzMx8sPEJ903Bw1eemDHcttq9eGt+aUalqVO5sl/x3e5fSqsb8bv3lunezySp+eefBY1dRFGXmSenbsDoS44LThgBANBowqlW0OSsCm7VNarbuDmoqqg1P6v+6f+d41q+XqD7VETrChw7Y5oGi578zG7E9xLm6G42d0PsiP70nKa5zdvBhro+NLe14wkN5y5aTClMr88z1+9I+n77B8sxdk4JVm/TN/sGOickw+S86PwnZnuSrrq3YwAf5ccPMpZuMFi21Li711XwD2bGK3NLNO8l6pWbTUvOKldhs81WY1UyKz/ljG8dYDF1d7H73En3ixBmbv9whSfpDv/ndN0DiJ0yfe123PHxKk/STiXTgNILfv/eMmyPiJmjYI6Ed06rBKGndJ7fFqdVWbLKNKFBLpgFGnUeeaMnGh67sK6yHg9MKMQ3xdWmczM8RNhgEic86qOgxsqkeIcX/hCPV0I0T+E5szdUdUxS+EFklKvS6kYUuXCmzGlD+wMIwfjc0Be7e0lFjTArvYJ1ttR4c5ip1x6KPl1Whro9rZ7mERQTV1fg0cnrdO9LFYweL2gc2msGs+ZmYRiDdVUKpperylrnu+1pia++/+yVRXhzfilusOD5z2iVrW5vq+5zbVUdZtoiZruCx/jV5AfZjqR6xPWayChXz5mw8TbDtWceBsDeAOLdX37bFRkAdwtzNikkeuaa6p8Yhn0qQvaT8IQUJcx2XgQSJUowjVl9xonekylqYs/V3CLtlaNfvLHYfuYGJM4Ws6PU7c5wJqBekh8s7jxs+c35pZbzFQQrhGHyxGtS61p7jHH/+AKU1+71pC+MjHLVq3vmw2X1UB9Mm1BE7Cgk3zt6gG0ZrGDHLPDj34Z7L5VZGpvDv1Exd8mFJlgQosG2Wv8cGdk+R8tEvER/l0l3ybRPetb6KnNCGcK6CpEtV/QZflMm5StNBplYFFzE7wm2IEuvuioyMxZvqsFbCzbjjo+9OcYoOspVN33lqmc345+xT4/0uGGetbXjiv3MvAOSlMioojdgODMv2L1igvVZ6co62dcTVohyy94+G/nuIzMdp5HY9/Pi7I2Gx5N4WVZMd3c+9NkfLN6Kk+6fonnPCydYr+pssBfCQzYrtH73AWHqchITP145yImMctVvH32v8ZecdIjl9Iyqy2+/f6Tl9ARvOf/YgR2fw6wYZzMz1u3IHEjF2f+Z4ZEkglMI4t5ZiFOwrR6PTl6HP//PnPMZt8ciy7fUupugIU7cuIeg4wmBCG5DRBcT0XoiKiai0Rr3zyOiZUTURkRXq64PI6IFRFRIRKuI6FrvhTUfNGFGGoVX5rWMXladJaU1WL5ll6U46rqsbs/c7BMjo1wd0q+X7bhanYH64d55cee5BReecDBuG3GUEsZ2lr6SmFHL5qESgdCvV6SOZROE0BKVtk3wntZY3GGCkUl2NqxyOnWE4cXKVRY8VkcQUVcAzwO4BMAJAK4nohNSgm0BcDOA91Ou7wHw/5j5RAAXA3iGiPp7K7F1cv0dA962H9e8tAA/fmF+x/e29hjGzCzqcERjRgYvVicjo1y5/W7UDeURA3t3fB778zM6PndTBerb07+BvdWBT64NlHLs5wqCeSz0YtkwYBacwegsB0b9iNGMbt7oiZi2Jn6eVdH2BhelSxPCER8vdeaGuYsHHa3UQZwFoJiZS5i5BcCHAK5UB2DmUmZeBSCWcn0DMxcpn8sB7AAwEB4iY4/wM275NjwxdQOenpZ8Fp+6DfPjHMvoKFcueyHS01SJOu90VSlXqx+8yL4AHhMKcwVBEALHbDP5UX6ZrvvuD5dsRVtM3D/nBMxIlBqjXiRT/zulsBIAkL/ZmnmOnyzZVOMovifKlUWNMQt7+sEAtqq+lynXLEFEZwHoAcDemQQeIgq0vwsATcrRBXtajJ2jaekAcohwCnaeh9HLTmxw69bFu8djpBBZ9hao/A2yEh99UB/f81x41wW+5ykI2cIXBocwV+9u8VESwQ8ufmaO4f2S6kbde8mettLvf+JwVSiVUx6Y4vrs8sdLyxz1kZ4MEGXgrfVULT0VIjoUwDsAfsHMmrNCRHQrEeUTUX5VlX2vklrjtro9rXh8yjrU7pE2Myok7bmCN3U7MsqVH5uvRx5/MAAgMWnb1QsjaxNYXokKwXSWnzMTiedzyH729+EJQjYis6SCHusq00321GaBtQaHZqsVHSt9sd3yWN/UhljIyrI3K1fWyEIrlTIAh6m+DwGgP+uTAhH1AzARwD+YeaFeOGYey8zDmXn4wIHWLAczPfKPl27F87M24sMlW5Ouh6z46uCOlA9MKERbe0isHXQaHb/7xsgoV25jVGF6KK7dLz7RuhfCIEgsbwbp/csPd6VZ2LEIgqv42QbMWLvdt7y8hoiuUbyOxYhouEG4/kT0CRGtI6K1RHSOcv0AIppGREXK3/39k94+e1va8cY3pRnDBdGztMViSe7hgx6sBjTXmoQfe0V8ZgmAo4nocCLqAeA6ABPMRFTCjwPwNjN/7JWAmR55whHMHp0zy6LgldXp0OrN+aWY7co5c8DXG6qQN3oiynbtcSW9BKnnXHlNTihXVh/kPj26YvHdF+CfPz7JI4mA7l3da6nDoHOoZfjV9w53Pf0oNFDZRhZ25FmPn69s/ArTE8xRoADAVQCMbeeAZwFMZubjAJwKYK1yfTSAGcx8NIAZyvfQ8/iU9Zi4uiJjuCR3xT6VsfYY49W5m/zJzATeOLSw9jCzrUVm5jYAtwGYgnhd+oiZC4noISIaBQBEdCYRlQG4BsDLRFSoRP8pgPMA3ExEK5R/wwL4GQKAdpcaho/y4yuAto9oMFlPtYK5Wb8i49vaL4cWCQ4y4fr9mWuHYb1N70j7dNc/8NdqEx6GGTX1qpLX5pQh+Lk5gehW0cPPdxaGSR23YOa1QIa9sHETpPMQdwsNxbtZYqPFlQDOVz6/BWA2gDu9kNVN2jRs71raYphXXIURxx3cedFEudpYtdtFybRlc4qjFD0o769YVB6zccKLmScBmJRy7T7V5yWImwumxnsXwLtey5dN7ZwebhQrM2XTVD4+FPGkdt6j/KKjXAUtgAY/Os2yUxtTWN9yFa7a75X5Xi40coLghDUV9b7llYPV8QgAVQDeIKJTASwFcDszNwI4mJkrAICZK4joIK0EiOhWALcCwNChQ/2R2iKPT1mXNuhPcmOsE++CJ79OuzZ/407bcsRsKFdzi4xNk0oNnHZkwouVK6tkoW4VKeyUgFx5Z0bV1Y2q09IWw96Wduy3b3db8ZP2jYpZoDmiWHitFraz8g7IGCbI56BerArDSprgnAhWK0HQhYimE1GBxr8rM8cGEJ+MPB3Ai8x8GoBGWDT/c7Kx3i827zTe6+DX6knqypWZbDdWGStPt76z1LY8YejXpE0OD1tr9mDzToPyFqGX5Yby0+5gpfnTpWXIGz0Ru3X2rQHAr97Ox6kPTbWdhxGdHrfde2mRWblym6AnoYzfYbpwVw8fgsWl2ud0hKHRV+PJDB9rfhQ8JBtNUAT3iJqDGWYe6TCJMgBlzLxI+f4JOpWr7UR0qLJqdSjiB5pmDUE0BU4Ga14gK1e5ifq1qz+f+9gsAMCfRx5jGD8q76ylLQYGo2c3/S0rRsQc/NAXZhcDACrr9uqGmbPBHYcZCbyuzdFZuXLw4qJSuG0TgkZfTdiUPUEQ3CfXqjkzVwLYSkTHKpcuALBG+TwBwE3K55sAjPdZPE95dPI6XPT0HCws2enb5FZbjENVxsIwmSCOnfwnm8eP6t82/J/TcNy9k11Jy3wcxj3jVmdccTZDYXkdGpvbTLli96MuR0e5ckDUZuC13rtRUQi+yU/GScG97szDMgcSfCFatUbwnbA1PA4goh8rHsnOATCRiKYo1wcRkXqz/R8AvEdEqwAMA/Bv5fojAC4koiIAFyrfQ8Hr8zah1eEZNB8u2Yr12xtw3Vjdo4RcZ3dTG16eU+JbfpkIw6RhxIYyOUd9Uyve/GZT5MacQPxsOSdim1m5Sp0cqN/bhvcWbbGfqYrLnpuH375rzuw39f14oWxFxizQ6LXZKQ+k89lLXr95OG55M9/1dBPlIizV2Uk5NYobgr4tp4hg/yD4SNgc6TiBmcchfmZO6vVyAJeqvq8AkHYOFjPvRHwlK3Q89OUatMXMKVet7TFMXWN8fplf7cIHi5MHXUGu2tQ0toTCLHBHfVPmQIKrWHnt931egM9XlOOYQ/p2XAvzaqObRdqOGa/bz2bp5l34oc75tHp5efV+IrNylQ0DPbODEavlvSPdAJ+RupIadUKZZ3R04gbfrwmCIESSur2tpsIdfc9XmQP51M+UGjkL8JnTH56GTQ48DbrFczOLgxZBSEE9ON+1J17PmttinleTvS3tOPLuSZkD+oSZMXqQE3Kp8nltGhgZ5UrQJwQTakk4MZ/Q+y3qShnFJfcoEuYZN0EQzBMy3xCmmL3e3Q3sTjHyZCbkBmFyxb6tdo9jpy9uymbHLNATTP4orXGkm9LlhHKleYiwahQftn7HqrIUBltwNV7MCIRNgcwFRIcVjJA6GR3crMsy6SIIneitxkRpEtiNFSXDc65spO92/2L0OrzoynJCucrUFyQeup+DBTernVll5s6Lj3Mx1xQZVMXT2CwwUzrWrucSRPQ4Ea0jolVENI6I+muE6UVEi4loJREVEtGDQcgqCEJ4iNJATw+zpo2C4BVWJo6DWC2++Y3FeHtBqeV4kwsrHefdbtDG+DUhwwzTA3kxC1Qw6hzsdBzqx5qIH7YVIDVmCkKmAnxg7x6a17914L62ZNLDE7NAoo5nEP1hgm2mATiJmU8BsAHAXRphmgGMYOZTEfdmdjERne2jjEKOEOLmUkjBzTYzKD3ttveXB5OxINjAyblPdpm9vgr3jS/0Pd9QoeeK3URwN1+ZY+WKiLoS0XIi+lLjXk8i+h8RFRPRIiLKs5uPl8U0McPgpzcgY9fq6XeNwptWLnUS+dePTjYX34C8Ab07Pt949rdw1WmDHaepRkyQAGaeyswJw/+FAIZohGFm3q187a78s1V9smCyWxAiR1VDs+tpxlycRpdmQcgl1OMx08MQzr7+s2zXHtv7DoNwZBH0mNGNlavbAazVufdLALuY+SgATwN41G4mTvqGTFFjHStX/r0NI5n8LhRdXCgFd/wwfkr5gD490btnNzx17TBb6ehVQtGt0rgFgKZrL2XCYwWAHQCmMfMinXC3ElE+EeVXVYVr87gg5Cqz1+9wPc0sG+cJQigpLK/XvP7s9CKsKqu1lFbdnnCZwX7v0Vm4+sX5+gFCpk2miuO3abSjYTURDQFwGYBXdYJcCeAt5fMnAC4gm4aObj8YtRQdylWY7QINCMPJ8fv06GoqXKa3qPcKiLLpVB19iGg6ERVo/LtSFeYeAG0A3tNKg5nbmXkY4itbZxHRSTrhxjLzcGYePnDgwPT7MiQTDJixzn0lQPCmPXfTRCkb9m8JglmsVMfq3cqqM3XWOWbg6ekbMGrMN6bTyS+twakPTcXkAud7oYywWpPXVTZ4Ioer6Lyw1N+aFMyDwaXTNYtnAPwdgN4JhYMBbAUAxZypDsCBDvNMI1MBydQZdDi0cEGWsT8/w3EaVuVgVSV2M117OOt49QYWIdAffYGZRzLzjpYqFwAAIABJREFUSRr/xgMAEd0E4HIAN3CGgs3MtQBmA7jYnix2Ygm5Qk1jS9AiZCVeNHVvfFPqQaqCkP3Y7QeN4v3hg+WYtLpC814sxrj6pQUAgIUlO+1lHgBhGC6wDXtMNvjmBNvKFRFdDmAHMy81CqZxLU16M+ZJes9rcP99MgubgZiLDi369OrmOA0tBcOMcpEpb71i48aakNfrSskOSDzNKrQQ0cUA7gQwipn36IQZmPAiSET7ABgJYJ1/UgqC4ISwTyTlaPMrCJZIWH5oWYB8sbIcv3tvmWa8va3tnsrlBhV1e13dx6nGLYsZK+1o2FyxfxfAKCIqBfAhgBFE9G5KmDIAhwEAEXUDsB+AmtSEMpknAe6bKKkVmA6HFl6bBXqc/Kf/9x1vMzCgr6LY/fJ7R3iSfhhMH0PAGAB9AUwjohVE9BIAENEgIkoc1X4ogFlEtArAEsT3XKU5mzGDDKIEwX/83Ptrh1yd3BJyE7vmY7ZXvOxFs4WdlmZrzR6c85+ZeHZGUdL11N/7TXE18kZPRPGO3bphjCSavma7Er8BU026ijf0/Odzu2V7mYWZ74LiCpqIzgdwBzPfmBJsAoCbACwAcDWAmZlMmfRobbf/ZELj0MLjl3vkwD742beH4v1FWyzFO/Ww/Rzn3at7V5Q+clnGcBlNF3VeQUS3w7mK4hhG63o5gEuVz6sAnOZSfm4kIwiCBUKuW6FoRwT2XQiCT+hN/Ht/zpXzhsKOiNvrmwAA84qrDcN9sbIcALB4U9p6iim+Uvabzd+4U9e9/E2vLzadXup70jSrC5Mr9lSI6CEiGqV8fQ3AgURUDOAvAEbbTbdRxwXkgD49HCstbp5zdcSAPs4TcYCdn9A1RJqLvrdAwiH79QIQLnkFQRDcJOyr9D97RdP5qCBkPaa3P3DHf5YH7OpJzZ2NLRjxxGyUVO3WCmktYZdJnXw1Mxlrp2lrN9BSv96g7+XYKC8/npwryhUzz2bmy5XP9zHzBOVzEzNfw8xHMfNZzFxiN489Ldp2qP326W5CPuP77Yo7DjdWrg7ZrxeK/3WJ9k2TyWuJEfL+1jR2zTuJgDduPhNP/fRUHKBzGLLgLrJuJQj+kyVNvSDkBHoKlxtmgVMKKlFS3YhX5m6yl5gH6I1FzfxcK88kkY+T1SRmxqqyWtNWOGtd9Ibo+sqVV5w4qJ9naSfMAt2aMezWVeexOigk/ffNDYXC6BUc1K8Xrjo97dxcwSPEKlAQ/Cfse64EIVcxXTXVrtgNgq0uq3MijYO47pM6Xsi0V42ZPfc4++WqCowa8w0mrCxPki/ZQRp3jP3/+MFy1/KOjHJ1zfDDPEvbTbNAJzz2k1MAaFfgg/v2wuw7zs+Yhp1+2c8TpC4/ZZDhfbUkZx9xAHp0ixdRGXAIgpALSFMnCOFEa8JR0xqHzc2lXzFmHpZv2aWfn2Eq3sx+btm5J8kJhVNY52GMnVOCEU9+rR8vcUSSg/Zwo2JOuXHHbuNzrjwgMsqVEzKZop17dNxD4U/O8HhVRPUyTVfSRFQC8gb09kAo+zxwxQmWwj985Yk46iDjPWnqAv/hrefg8AN7p10XfEJWrgTBd4Ke5BMEoRO71dGsq/KKuqak70FbjJz3+CyMfEpf6Ung1BlfpkPodc0PDR5QmgKlentJngQzCecCOaFcZSJvQG+UPnIZTh+6f9CiAPB3JckJlguoCQ0p1TTzhrOHAgAG7ef8PDPBGm4ffyAIQmbC7tBCEHIVzf3wenuuEn8zaEux1Pumu93M7URTazv+89VabK3RPBbTJvF8l2+ptRolMBjA3eNW694P2zlXoSHTIDDomQCrBNG3fuvAff3PVIPUn/7/zslD6SOXYb99MzsuEQRBiDqiWglCdNAcfxJMK0lejk+XlNbg5a9L8PS0Db7n7VVL5oZTDD/a2MgrV2Zm+Q7u18sHSYLDaQUhAib8/nuY+dfvuyOQCX597uE6wvgmgpCBqE1KCEI2ICtXghAe1N2g6ZrJ5hxaAOkrV25ajLQppok7dRxHPDZlnWt56a3QuT2OcCM51vnsJpFXrjLRt2c3fHjr2UGLYYqODXwa91L7249+c46ree+3b3ccMdCfM7pKH7kM91xmbb+W4D+iWwmC/4hqJQjRIZNZoBUamlpRWF7vTCALbN7pprmgRbwYYIRo0JIVytVFJx6ie2/UsEEY1D/79usM6t+5GqdWvKKyX0uPqMsvCEL0IKJriKiQiGJENNwgXH8i+oSI1hHRWiI6R7n+ABFtI6IVyr9L7crSJSt6ZUHIPqy4Yk+QaeVGff/mN5bghlcXad4LkoJtTlzGd5Lp55h5vpn2sCXubti+G0/pmEP6QVY041cOGxy0CIFiugIGXFGN6s3EP34Pz143TLwChgizB+8JQhZQAOAqAHMyhHsWwGRmPg7AqQDWqu49zczDlH+T7AoiE0yCEB30XLHrfDGMv3Sztlv2JaU1uOXNJWg36YHQbdTu4s2O0ZIn/d3D7BNYvKmm43NhebJySKr/vSIrlKtsw2+b+zB05ScO2g9XDhscClmEOKJaCbkCM69l5vVGYYioH4DzALymxGlhZgsus8whE0yCkD1srGo0vG9mDrN4x27MXLfD80N3naD3OxjWxhKd22PsNYSF29LNKmetr0qTSY0Xba4oVyHEyXv2s2OWMYAgCDnEEQCqALxBRMuJ6FUiUh8+eBsRrSKi14lI81wPIrqViPKJKL+qqkoriDi0EIQQkUn50d1zpcT7+yerOq61tcfSwhktRrk9wdnU2o4PF2+xbpVio03yqhUzEp3B+F/+Vo9ytkbklatc6YaCtND6wbEDNa97IVIXGViEBrEKFLIJIppORAUa/640mUQ3AKcDeJGZTwPQCGC0cu9FAEcCGAagAsCTWgkw81hmHs7MwwcO1G5X5RBhQYgOVrz73Tu+ID2+0aG4TjvhlOhPTduA0Z+txpTC7RbT6UxIr3kyeg52mrREeqlx3T5/06txTuSVq2wirGPZN35xlivpmNGbRLcKDz26SfMgZA/MPJKZT9L4N95kEmUAypg5seP8E8SVLTDzdmZuZ+YYgFcA2G40Zc+VIIQTw7ppYvCipdT4Oe6r3t0MANjd3OZ62lGbjPXaEVzWjJ4uPVnfY2DkkL5VCAH77SMHNwtCAmauBLCViI5VLl0AYA0AENGhqqA/RtxBhi1kgkkQIohd7cIgmmMTYZfaEjO/TDeMy1qXoVlgiBS8rFGunr3uNM3rIXrWKViTLO2EaZ1KZ6cu2a3AXowBZFwhCILfENGPiagMwDkAJhLRFOX6ICJSe/77A4D3iGgV4iaA/1auP0ZEq5XrPwDwZ9uy2I0oCIKnaA2VrKx6aIVMPURYjdtmgW6QabzIzNhasyf0k0TqR+u2qSEQtyHPCrp31dYTw/R+bRyR0EHqyw+Dm2xPJAh7jRQEIetg5nEAxmlcLwdwqer7CgBp52Ax8889FVAQhFCiNzA3O2APfiSXmfvGF2YMkxiSvrdoC/7xeQFOGtwPAHDv+EJ858gD08OH5JczjJ2K2CVrVq6iQPL7S1ciQqAvBY6oVoIg5DLSDQhCeFArAV7M/RqtXNmV5f7xBR37qzTT8XCwmTira5PKBf38jTst59vUmu5ZMZ6GA+GSU+pI0IvnkfXKVXgXQpy9zDC467UqgWzUFgRBMEYm2YRcgoguJqL1RFRMRKM17p9HRMuIqI2Irk65dxMRFSn/bvJaVq26mRjX2K22xq7Fzccr2NZ5UO5bCzbj/gn6q01Nre0mpTOPXytRRvnYkoDIkzY38spVUDrGwL49LcdxIqrdCmgGu3J5UZVCoDMKgiAERljMZYRwEoYtAW5BRF0BPA/gEgAnALieiE5ICbYFwM0A3k+JewCA+wF8G3HvnPfrnS/nBx74s0jDaIL68v/OS/oei7Hu4O5eE2Z+TtFaAMi0KODn8E/9vtpl5Qo4+qA++PPIY4IWA/NHjwhaBE3CsKLlBFndEgRBEARtvNgfEiBnAShm5hJmbgHwIYCkc+eYuZSZVwFItRO7CMA0Zq5h5l0ApgG42EthtYZXickQu5MiRsqy0RlPmYZ6XxVUdmhuFXV78d6izbbkyySTFexMDKTGcFsPIgAXnRj3Nn7W4Qe4lm6kHFrMHz0C/fbpjj49u+HkIf1wy5v5gcmi50AjCEQdEQTBb+74YfCTXNmIk8HD1WcMwSdLy9wTRggd8QFq1vT6gwFsVX0vQ3wlym7cwVoBiehWALcCwNChQ61LaQIz9VZTObNglVS2ay8O6tvLdH4JNmzfjXvGFWDk8Qep8s2cwDsLSk1P2Hu1oJqarlvZqNPp3jX+Gw/bf1+XUo/YytWg/vugT8+4Pmh2hSOKKyFWC09UJrLM1NEu0XtdgpCTnJnn3iyf0ImT9vzX5x7hmhxCOMmylStt58gux2Xmscw8nJmHDxw40LRw8bjGWSbGmHbfi5GSk3rrqhfmY3JBpb2MALSrhDSjDN07vhD/+Nz2kX2GaOWvJZIVYywrK2Pa+btXuSKlXKnJRrv0xG/SbDEC+rl/vOBoX/OzY9V4/rHWGksh/Pzk9CFBiyBkIOomyNmIvJLsJ8vGPmUADlN9HwKg3Ie4tvhg8Za0a7qu2E2+JqNgWvW5sLxO954V7JYis/lmCqaV/z+/XBM3Z1SHS125cmkw7NZz1COyypUWt56XfbN2iRfvZYNqVLgO7N1D996xh/T1QBZrJb30kcvw5i/Ocl0OIfv4x2XHBy2CIGTEyeBBdKvsJ4v8WQDAEgBHE9HhRNQDwHUAJpiMOwXAD4lof8WRxQ+Va4Fgd4xmtOKlubri4P2rowbtGEXLBf2s9VVp19L2XLmQd0lVIx78Yk1nmuIt0Ji7L43O4MmsnW2YG9KDbHhMFISgsLvSkhrthm97Y7MvCEB0zLyFYAjzmMAqzNwG4DbElaK1AD5i5kIieoiIRgEAEZ1JRGUArgHwMhEVKnFrADyMuIK2BMBDyrVgMPVetMwK/UOtyLntOt6qshYzaUfp64SRi3UrUg4ttMimmbpE2TTzm/z63UHPbghC0BCS29wfnzYY7y1KNw8RBFeQJlcwIMvMAsHMkwBMSrl2n+rzEsRN/rTivg7gdU8FzEDnnit/3ktisq9s115H6bgtrpn0GpvbsXJrLU49rL/pPWpeewv0isiuXJl9wFG3Qe8wCzTpMSXMv9eU0hhi+YXcpGsEvKw8cEXq0TCC4B1D9t8naBEiw5l57h69lGUOLSJPhyt2H865Uudz0+uLMwc26Lpsu463eF3N3eNW48rnv8Geljb7yqgHZ756UaUiq1wJ9jllyH5J38O0MT2K3h2F7KZbl+RmMoxjm6MOcn//oxAMUViZmH3H+Tjh0H5BixEJEh6O3UKsSfzFDccUyen5+P4MnEHY1m0yRGQTJljtMTY9SbCperdJyZzj5psR5coHLj35EJw02N2OyI4Kkui03//12RbiOOcAA6cYQu4QIh3eEt0isHLVJYCWPKrvM+wENXa24vClW9cuGPOz0zyURtBDVq7Cibress4USfXuZvx3ZnHStWxpRlPbLaPfRUSmlcx3Fyab4BtNPtlpO716/pFXrsK06qLHCzecgS//cG7GcEblIvVer+5dNcOZWflxeyYtE+qD6zIRgdcp2OSdW76NB0edaCpsmMpB164hEkaHLmF6YIIjglKuLj35UEvhB/UX08BAEOUqlKgH/S/M2qgb7qlpG1LiBYNXZoyJMblRuBizg5Uze/HspBmLsWnHG6lEXrnKdhJDplQt/4DePXDOEQemhT/2kD4+SGUPM+M/GSJmL0TATd/JC1qMDo4Y0NtUuNSVqzBa5YhyJTjF6t5CvQk+IRm392z65ThBsIb6teRv3hWcIAakrq45TSPpesfes/jfhqY2/TRi4SzHqePs4++bjJFPf20rLVGufES9ytbFhQZ3uMZG2Z8OP0wjZLBYqUMyRsxerLalx7u8pyO14TRb1qKguETAclEwiZMhh5Oiqhd3v326209UwN8uOs7V9MI3JBX8ZtLqCsdpBKnbxJhtK1d+it3cFkNJVaOtuKJcmWDEcebN2sxyp4MG99yjByR9V5dRLTPJw03O0GvhdwUUhxZCgktOOsTT9M2aFKcqV2HcUN5/X/8HwFJTvSGo8qU3iWDVLOaaMzS9ZoeWC0842NP0jz3EXWczYWx/so2m1na0tscAaK/wqD31+e2KfcysYvzuvWW24qp/i1fSmulXnZgFLil19ygzIvLEiVDkz7nKhNMBQOkjl7kih5pzjx6A/QwGQ+rXTEQAd776kn9fanl2cvi39semanvatxtYkTcCiwRCRElt9M0WtSisCsn+l+whqKFzV53Gt93iKChqbXjvHtEybxSHFt5z3L2TcdLgfrj9gmNwQO/0sdrXG6o6Puu6Yg/5e7KvpGvHSyRnJt0YW29XErw+b5OteFZYsHEn8gbs6yiNrFeusoVEOdQyJwx7ZyYTbUIYSG30zZr7RcFpjiA4Rc9UvT1lNH/v5dl1plp7xPqnKLjqzwYKttXj12/nmz5yIArvJXnPlfM07OLELLC3yw7Z1K1eQqLrX1no2BoksmaBWTlgV36U1aGcl8/CzaTF5C+3CVvnY1ZnSt2QHq5fEUfqVvbgrD23Xw70HC+kKlfZVtKuOMWal0Qr7OOB04+sHPuEmK01e0yFi8J7SVKuYjbTsHhdixgzYjbz/4HL23T0Jllr97Q6S9dR7BAQpYb+mIPjnvyuOn2wYTi362jQE+9Wfo+sEmQvUeh8tPDCLNDrfR5+kE1VlYiuIaJCIooR0XCdMMcS0QrVv3oi+pNy7wAimkZERcrfdG9DpnFSUezH1SvnbanKVYb3HjVFv5vDoxbOO2ag7r3l910Yz8PFRiSq7Wi20rnnKmBBLFLd2BxY3jEG+vaytwLVs5vLagvBkxnTyCtXUeLQ/fZB6SOX4cenmd/w29kkR6zm2iRa3bK/ENHjRLSOiFYR0Tgi6m8QtisRLSeiL/2U0WtOPUz3J1vGrkMLNxjYt6er6WWTohMQBQCuAjBHLwAzr2fmYcw8DMAZAPYAGKfcHg1gBjMfDWCG8t0WQQ2ezZbzKHjP9JPHrz5F917CXf31Zw01ldZ3jkw/XiWVMLqwzgYenbwOt3+43HK8sFlkGJHk0MKutz7dpSvz6cVijO8bTEoY5+/u804yC2T30retXBFRLyJaTEQrlRm/BzXCDCWiWcogbxURXepMXFtyupre/VecgDsvdte1ql386OJSC5rZQ2DtIv22IdMAnMTMpwDYAOAug7C3A1jri1QOueLUQZrXU1d33rj5TJxvs0HW4v+zd99xclX1/8ffn93NZtM2ddMr6aRCNglJIIEkQEKQjtIJIB0UEL6AKCpYwPJFv2LDBhZUREV+gDR7QSAgUkUDBAihRJogJe3z+2NmNrOzd2buzNzpr+fjkUd27ty59+zsPffezz3nfE7ohBZlzmgxY0TfTq8LLc70EdGmuK8F7v6Yuz+ew0eWSXrC3Z+Ov95f0jXxn6+RdEBUZRvQqzmqTWUUfgxi5vef2Phmp9fzxw3QvHEDAtc9d69JofZZyaI8O3zmoBlZ16meW/nq8rXfPaFfPrChy/Lw33d1/WXyn8Q3TUILSU/9+7+6IeA7jGrfUvQthKnnvdSW+ry3W8Bn35W01N1nSZotaYWZ7ZKyzkckXefuO0k6TNJXC9hfJ+l+/Z+dujCqXQQ6btE4nbr7+KJsO/E7VXJ8Mb6tt8a35ZnaPc0vNm14a7ZVIMndb3f3xMx8f5UU2ARqZiMlrZL0rVKVLYygOvuTk1JPGdtNHdZa1Juv8PNcFa0IofRPubku9IERLQ+ROEzSj5JeD3H35yUp/n/eAwM2be08GOG35+6uFdOim5Yg3UDtsMf5hMGZJ6p/9Pn/hC5LvTTChK1yYb4PUrGXVtjvuxr+LMllzBZDvPFObmOO3KUVX0zb8N/JNs+/vS/qlluzzvcmWyLKcJN3cOUxiUdU3eL/gpJRJu6c+0rKHtIWKPL+mGUQ9Kethoqbr5s/sFvHz4y5Cu14Sb9K894XJf2PpDyHjJbGibuNU/vYARkD6nGDOt/IRZpgpYyTCBdSn6kh+TGzO83s4YB/++e4nWZJ+0n6aR5lOMnM1pjZmo0bNwaus/GNzmMh+vbopolDMgc0SXvIukZLU3CShWzn3m8f267ffGiJFo4flHG9tzdv7bqwgq9fqWPE9picY+t4hBUy9UFKkFq+F6hE2erF62/HgpBq+LskFzFbkHLWjx/Iuo1U724Jd8sRm+cqvy8s6par5D+vS9qcb6aNFAVFIvFxHQ9IeknSHe5+d8oqH5d0lJmtl3SLpDPTbCfrBafLZ/IvdlVJ/OGroN6mlUsdqvfYKswNoJldJGmLpB8GfH5fSS+5+30h9pVzvYvSRat2TJuhLHBp0sKJg3tn7J47MMRNStiB96kX19JPrN1ZocFevVYxd1/u7tMD/v0yx02tlHS/u7+YtOxFMxsmSfH/X0pThqvcvd3d29vagm/iNwc8OU3N2FeIdM+Msx0XjQ2mHdqyB3m5HJ+VmASgsSG326Iw55Gw30jfHt105PzM47Oq4Sa+nnz3z+skda1XYdtmStkSmbyvbMHV2pTuvVm3ncNd6jaPvltivlLr79b4+bfQe9GCgit33xof3DtS0jwzm56yyuGSrnb3kZL2kfR9M+uyzzAXnC6fKaTgJbTz6OgG4OfrmAVj1WDSkknRprBE9LLdAJrZsZL2lXSkB59lFknaz8zWSfqxpKVm9oM0+8q53hUi3Ukxp5NYfBurZg7L2D33A8smdno9vG9LDjvprLEojeHpz2ATB/fOPJdQyve1x+S2HCfqjia8Gta3bicuPlyduwRK0o2Sjo3/fKykXAO2DkE3KflOuJmLbIdFugchhWy3EpMzRNUN+LhFY/P6XLb07ZX4ndWTdA86KvFBQap7173a8XP+wU3h5SioW2DEX7RZ53uTRMtVoRk+I7ltcPfXJP1O0oqUt06QdF18nbsktUjK3KcgR5Xe0vG9E+anfe9npy7QJw9IjUel/7y9JWDt/E0f0VdPfmaVhhZwg5lQ7O+7wv+cZWVmKySdL2k/dw+cfMPdL3T3ke4+VrFxIb9x96NKWMySSDxtStzwnbW8czCVGsj96oOLu26jjN0CM5k9qp8WTUifNSy1NN84OjBzePrPR/TrDO9XO8GVmR0Y72GxQNLNZnZbfPlwM7slab2ekvaU9POUTVwmaU8z+1f8/cvyLUvQzUvUNxRBsgXd+RYhU6tbJd6P5lrfg1Zfd9kqfew9xUn+VInfWS1LvZYc/e3UDlox+Qa95RoKka24L7z+TprPpUlokeOvn+/3Ff1URUk5ud07xlzl+zApoZBsgW2JVNBm1kPSckn/SFntGcWyKsnMpioWXJW+/1EZ9c4wm/ScMQP0npldM6XtEJAwopIeVuVblFCHaqVHy+V1paQ+ku6Iz7PzdanrDWClKvQQznRkdMvSvNQ3YBB/2Ital26BJbi9ac7w+6Te/DXnOM40ihoW5bw9lcDdfxF/KNHd3Ye4+97x5RvcfZ+k9d5y94Hu/nrK519292XuPjH+/ysFlKXLsrCBTZhDOtdryWFzR0mSpg7tE2r91OyGmcZh1MJRFOZ3yOUGOtOqu00cpJZu1T+uvJKl1r/U6vKXJ14u6v4i3XaGa1W24CZdvY2itF5At8DIx1ylvE48DOqWY/fgVIV8epik35rZg5LuVWzM1U1mdomZ7Rdf50OSTjSzvyvWjWJ1mq5M0PaDbfKQPpoSv5AlntDn8rWNG5RnNr8MZSpoGzlUx1q42BaLu09w91GJuXbc/ZT48k43gEnr/87d9y19SXNTyN+8kNNJ6FTsZTgod2jrrZXTgzPEmXWdDyeXiVvzeVL6ndXtmjVye0p4noEUT9AhHeWYq1z/dgvGD9S6y1ZpcGu4ng+D+3TXbz60pOP1u5vTB1f5TiQaqZTvo5KP7e+fML+eu+OWRJdJs8N+sALvbLdmyM2Qb8vRH/5ZePtIIQ8oow4hGqzzOXdz/EtrLHBy8UKyBT7o7ju5+8z4mJBL4ssvdvcb4z8/6u6L3H1W/Gbw9oJK23n/odar5BOlpLQ1t5CshzeduauueN+svD+fiYkACIWZMrSPjlkwpuN1ppv9roOEg6VuIsrTb2NEG58zpv/2TYTYxnvbRwUuN0nXnpg+hX02+dTfBrNO3SRyCeaQm6A4KsrgqtjMpJH9e3a8HpKhO3q2zIPlkOu9W5iHFRV/H4IOm0JmvEvVJVV2BVTZE7+3Ju17+Z5SHtkQPNVCrpvLN0ha+1JuiTay2bLN9a0/PSUp9jskguuKGHOFIijgbNyre5MG98n8lPEXpxV3PrB8cRGqYfFz6a1nLdYl+3cda5hN8k1MahbN1Jv98SGymhVzzNWxScFjwtXHze34uZAMmkE3c7kltAi/bsKiCZV3E1yrgp7qvrslIL15iYwdmFtPCJN1Osb+77DZadcd1Ls0EySn06Nbo6YN6zypdq71I8zqH0xJsJNxe1wEy+q/m/Ib815tnbLyLW8Uh6d7/g9An3vt7cILkOSP//p3p9eJMVdNZewWiCIJOngzHYhBF+Ns9Wan0f0zrxAXZf/uUE/4eCJeUYrVAhpW6vGQ7rhOPbSCxi123XbIMuRxSLb16d5lWZ+W4MlbU2V7AlpoDcn15u3oXcZ0HdNGNS2aoGP87Qxd65LtEGGX8IRZo3LLeJt8eJlJ/XqWN4DK5NazdtPg1hb94bw9OpalVo9CMo0m9OvZrJ7NmbMAojJ89tbHO70OGwRUV2iVf8tVuoeNucZq+caixW7F3xLPFli2hBaVozKv8oMDbq6yCXySkHhCH3Q8leAJ12HzMs+5ETUe2lWWA3caGdm2okwEkThMgupFt0aL7Onv1GGtumjV1E5o8lzoAAAgAElEQVTLwvwWxXz6XOimc/140N+Nalo8QdeBd4Im5g0Q9rj7UprWpHs+vCzU5zOWIc3PWVcuof1mDddNZ+6qMVla5a4/ZYF+ecauGdfhmlVbnsxxfqeEKuq5Kyn/MVfpusvlcn2PtVzlt/+iTkXg24O3QlvIaiC4qkw3nL5IXz9qTtb1Cjoxl6AZOpIn1rl0gcpj8yiuS/cPTil821ld05vnI9PfPOwJOHUbYZ42h7kR/dUHd9PMkbnPVZfu6d5lB83IXq7U12G6BYYtWMD2skmcZqrs3qFqubr+jc5ePinSfew/e0Tg8rBJKzKywvofBN27JSdTicLMkX01fcT2bSafZ5LrbvvYAYGt0Mmi7m3BNbC8Us9zb20K92Cj+roF5ve5hnTBVYlarqoliK3a4GrOmP4yk05avEPG9crVzWx4vx5akSbbVylU69O0ai13LTt6wdjA5blOrjttePgbpKDjwJQ+2AoKQLplyfZTzEMtW4+CQlrxCs1emOs5sUyN5nXLveuxuePw1sBxfJUo6NDIKWNsYOWP9oDL9PR7xohoA7mEKrv3rlv53rxX258332Aw3fUnl+15AVfAYs/5F9XWqza4Gti7u576zCrNGzeg3EWJTEt8ZvaezU1dLlCBh2KmLGvVVtPjGHNVPXJJ9LDjsFYNSfdUPI8/eRTz+STqWzrpnliHqVvpyteRiCPDNrzj/3SBZFBCi8xfSJ+k+faiuE+lnhbPNvfAuvWRfXeMZPuZ5l6MQlRx0FG7bO+SHvXRlqn+ZSp/4MPcqAtH1SqvPG+eNueZZbBcyjnmKjbPVX4FSE2VHyWXR3bvXLXBVS06ZM5IfWjPSZ0yC40eEEtp26d7wGD4MkRQhdxUcc2oLbkM+Lzlg7tFss+0CS3yOLoytWzddeFS/Tpprp57PrxMYwf2TLu+1Hn6hHTlKVdQkppkIJ09Jrd1WdbRLbBKH9hUm6BugVL2ibLDinKepN+eu3uXZbEW5vjP2SKtgGMqcVrp2dyUabWC5Lu9D+8ztcuyyFtxqWdlle/X/8wrb0WynVLZmucJPd1D0txTsee1+7J0v8xnnwRXZZZ8Xm5qbNCZyyaqR1JWoUv2m6ZvHD1HM3Lsc15J3XZyOiwrqNzILGzLVT7JXbJJvWkLk2Ez9fyY6bgc1reHWpOy+w1ubdGI/plvSpO3V8zWoXy6BSb3k8/0d5sb2BMgIKEF9bRoYt0Cq+MLHtLatW6bWccxtvPo3McrluJ3j3JQfNjS7jwm83exeuHYgsuCwuU9FqnKTor5BinD+xU+LnPfL/8p/2yBRQ+uotk+wVWFa2lu1N7T0ozdKkNlLmSXYT5bXaen+ha25erI+ZnHipStNSfPz4XpLR5FtsD0+8k9ocXSyYND7TPs34J6Wjwuj/zU/uOT8p90OpN0N5SNDab/d8au+vbquYHvZzJ1WB9JnW/+StktsFj3bt84ul3/L0PmwUQKaCpXeT303OvlLkJJ5HuYp6sfOSe0yLMEW4vY+zLKul9zwVWXOWGq/ESV7x+7WHNqTB7SpyjbTWACxcp3+cEzdNQuozU0ZGaxM5dOyPj+ogkDQ20n06HRaYLhsAFCjsdaIsNaqAmKs7yfS7XOlj0wjCPmbx+/skfIQCuBbIGl5V49162gtMyJoGjGyL4drb+Hh5zS485zFmufGcOiK2AamQbFu6TPHzpLH3tPuDFuYc8jvbs3ZeyBsnlL5zKdt/fkUNtFZai682OZE3dUarfAoM3f/dQrOW+n5oKrWhHmfH38orHae9qQwO4EYScJzlX/Xp0nhPz+CfN0+9nRpORGdZg8tFWfPGCGGhpM93x4mW46M/3T2F0nDEqbujVh5fRwN1NzxvTPoS0nt5Pw9acs0KFzMs/p9d72UXry0/toeL/M3QOXTGpL33UvREKLVKmrBm062/miKWm8zsyRffXe9vDzlwVOv1ctd/9VyN2L2pob5Z8uqPU66NA+aOeRWnfZqqzrThi8/eFdMY+xbNXvkDkjddyicaG2lVzKlm4N2mdGflmCN2/r/EieKlZdqi4Ve55h0qMb/hPJ9vL9too9iXCQu554OefP1FxwVW0npEIuIP16NusbR7erX8/m7CsXyW4T2zQpwtasQtNMo/iSLyKDW1u0Q1v6iTh/8P75Be4r9v+qGcM6DXAP9dkc1m0fO0AfDfGkOmOgGN9hn5b05QxzeCd+590mtul97aP06QOzz42VTXILg1nX1r0Tdh3X8V6X8jCJcEm5F3YefP+u4YKCKARdv3LNGlYsf75gadr3Mo25KqRM/7h0pb56ZPb5LYNs3hrbcbWMt0M0vvOnp8qy3215dq+78e8bItl/PgGLVNwxV7HJjaNRc8FVrZg3NjawfFDv8gVOUcnliU61Bcf1KOoHR6H+5llafPI5blI/kpzAolBThrV2en34vFGdXrtcp+4+PmM3226NDbr8kJlZW8qk7A9pUlsYxg/uHBAn5uTbbeKgLp8N/M6pp0WzzQt76DZ+cPZuq5Uon+QX6fTt0U0jMtSb1HPYoN75J90p9JqVyFqamiK/yhpCkCLsfc+G198pckmCRZ6BM8cNPvfa2yXZTxS+9Ot/5fyZmguuuoy5Kk8xCnbe3pN15zlLNGZg+lYBoBy2pIwoLcmT1ixjKcOUIDWgSBjYK/cHGBOy3MDuskPncWQLx3cNWs5fMUW3BXSpzXazluvN3N7ThqTM6WV6/6476Ecnbk9yMHfsAK27bFVOEz2jOFxeUI0Kuvno22P7g4Nid+nM5d4n0So6pLW7fn7aIknS0imxMYGrQoy9Wr1wrIb1zS172dyx/TvNoSVJvbo36eQlAXNYhVDo+W/JpMH62Ht21EWrYmnej180VvPGDtBhc0dl+SQqSbUFw1FmzCyltzdtLdq2H3/xDea5qhXpTstNjQ1Zb+AKFTQYudzoElH53i3BZImJ46CQ+8DUk+TSKUN05RE7dVkveX6qsG794G765ydXdlo2dlDmebCikvhuendv0vvas9+Anbf35E61yizWvXHB+HCJRDq+xiJmb8N2xUhoMTWlJbWYcumpkFg1+bw/cUgfrbtslWaNyt6S9fH9pumuC5d1WZ7p+/vpKQs1uE/6gCzfsSj5ajDpuEXjOlquBre26LpTFmhgAa1pKL3U46YMQ4NyUm1jxBJe+E/xWvqe+vd/9fiLb0SyLYKrOnbrWYt1+cGFj+cIK1Qqdu7aKl5qcFXMv1nU5/8pQ7d3w0skZ8lnF02NDWpOCcpO2z1zVkQpXKtB2N/54U/srcsPmRlq3SiD1Nj2qKjF4u519/0W69f92pE7hy9Dno8M6uxPhRpRpbFV0b0UUfBGcFXHJgzurffNDZcitxC51OHJQ4ub6h2FayzHWSMx5irlaCpk4s2z95wkSRoY0bjGbFkROyngwhZ2fjFUJ1eBwXCWg6vYR08l3LQlfseVeaR1r4Ty1xszW2Fmj5vZWjO7IOD97mb2k/j7d5vZ2PjybmZ2jZk9ZGaPmdmFpS57QrUdN1G3rFVrS1ixEFxViHp4+hXmyeCUoaXrvoL87D4p3DxJ56+YEv3OU7oRfew9O+qpz+zTeZ1MCf3inx/f1ksj+vXQ5QfP0HeOzX2i04SwmfzmjxvQqWiFXIYacj5rd/5ChvfNniAjWWC2wDo4X5VLLFtg9XzBc8Z0nvZjyaS20J/NVg+GtrZoYZruqz26pZ/LMZ+WvxH9Yl0FB/fJ3B3vnouW6ZL9pwXsM+ddQpKZNUr6iqSVknaUdLiZpaZuPUHSq+4+QdIVki6PLz9UUnd3nyFpjqSTE4EXMou6++s1dz0d6faqXc0HV5V+wqv08hVi3CCScdSaw+aOythCc/Vx2wOVYxaMCbXNTHUgMQ5hUErSicRnzCynG6k33t0iKTaAXZLeN3e0BoecDDlI8uS8CUGXrEL2kaqxgJPGgF7NGppjAoDEL5T8e9XwaavstnlhCS2yifqac+2J26dbuOfDy3ROvEU4F+mK9NcPL9O1SYlXkv3t4j1DbXtKyN4QR84fo6uOnqNDssx3N7hPi45ZMLbjdeL7zDcgruV7gJDmSVrr7k+6+yZJP5a0f8o6+0u6Jv7z9ZKWWezE75J6mVmTpB6SNkkKnoipyKqt3WbNulfLXYSKREKLGlOMJ5U3nbmr/ve9syLfbi77v/ei5WXbP0qrualBu08O16oV1u6T23TZQTN0wcqpGdcLez5MZBZLvjmqNjl1P4xLtPT16p7+aX861XbTkA8zO9TMHjGzbWbWnmadyWb2QNK//5jZWfH3Pm5mzyW9t0/QNsIotFtgqSX3SBjc2hLq+Fy9cKxGDehRUFeilkwtV0k/33D6Ih28c/ZJsxsaTHtNG5r3eDd66+ZthKRnk16vjy8LXMfdt0h6XdJAxQKt/0p6XtIzkj7v7q+k7sDMTjKzNWa2ZuPGjdH/Bqq+bnFX/2VduYtQ03KblRNFU4zz8vQRfTV9RPlSK/fq3tTRQoD6E/YeJVN3UTPTYfMKGxeYXI5hfXvoiU/vU7ZxS1HcNBfScpWPoJuGGky48LCkgyR9I90K7v64pNlSR1em5yT9ImmVK9z984UW5H/2nqwzl07Qgs/8Jq/Pd4v3G20f019rnq6sp9Nn7DFBzU0N+sCyifr4ftP07CtvSYr+eDplyfiOn1u6Napfz+jmsEu48oid9OY7WwIzHiInQV9c6kkn3TrzJG2VNFxSf0l/NLM73f3JTiu6XyXpKklqb2+vrigIVanm7nxz6Uf6hUNnVcyEi7V3r7JdlT3QQQa5/C2LcbMRxaFUrMAql60W8pSzVIHhZw+eqf/52YMd33lykWvtdOXuj0k53eQvk/SEu0c+0KBfz2YVMp3uATuN0BMb39TpSydo5sdv7/J+6m94+LzRuuepl/PeXy7XrnP3npz3fnJx4uLOc1YV43jdd+ZwSdKmePbUGSPze5BJUKb1kpLnlBgpaUOaddbHuwD2lfSKpCMk3erumyW9ZGZ/ltQu6UmVGLc5tSGqsWg1F1zl4uAsfatLoWM+nzo4wdZyAIlopTtWZsfnvpk2PHzik0o67BK/VyGn70KCq1xiOrISZnSYpB+lLDvDzI6RtEbSh9y9S7ORmZ0k6SRJGj26OJlam5sadOE+sW60P3z/fI3qn3n+tc8cVLrpOGpRc1ODfnbqAk0YTKbbPN0raaKZjVOsNfgwxYKmZDdKOlbSXZIOkfQbd3cze0bSUjP7gaSeknaR9MWSlTwZ0VVNYMxVGqlBSrV0X6mSYkqS+veMJRdozD1tGapcxqc6KW+FPaZzuYffZ8Yw/eWCpdptYviMZKUydmAsgUt7Sva0ZFE8RMkn6Mnn/JKo3sHzXOW+vXIzszvN7OGAf6mD57Ntp1nSfpJ+mrT4a5LGK9Zt8HlJXwj6rLtf5e7t7t7e1lb8Y3jRhEEaPbBzcBX1NTGKrRX7eFo5Y2hRtz9nzAD17RF918N6EB9DdYak2yQ9Juk6d3/EzC4xs/3iq31b0kAzWyvpHEmJdO1fkdRbsW6990r6rrs/WNJfADXla79/IpLt1GzLVfemBr27ZZtW5THPRSklblar6Wblq0ftrNsefoFsgHWoGF08c03mMrxf5lTiqVsr1QOWGSP76g/n7aFRA3JLdZ6rUrUopQaCXuX5At09quw6KyXd7+4vJm2742cz+6akmyLaV1nsPW2IbnvkxewrFmhEvx46ZsEYHTk/XGbRfM0aWUhHSxSbu98i6ZaUZRcn/fyOYmnXUz/3ZtDycnjpjXfLXQREgJarNBI3ABOH9Na6y1Zp1qjKPqlW42DYwX1adHQO2dZoLUcm+WS/S5V8Qjxo51iiqT0mx1oGhkaYBj2b0QN7dgRzv/rgbnltI1uf7zP2mJDx/eR0+IWIogtjjTpcKV0CzSz5Kd6Bij1Jr0rrLlulbxwdmDAxUCEPLxoaTJfsP53J41H13oxP8wFINdxyVS3BSuLGpZparoBk6Y7dchzTh8wZqU8eEBtDcsqS8frt4xs1vF/pgqtkU4e16utH7az7AjK25ft0bN1lq7KuE5QOf1jfFi2dMlinZwnMkiVumrcFZgsMvZmqYGYHSvqypDZJN5vZA+6+t5kNl/Qtd98nvl5PSXtKOjllE581s9mKndLXBbyPMqqW4QEAakPNBldRzz5dLImsYcWY56pSdIu3TNTy71gvMtWqShiC17t7U0e3uUq4oVoxfZhWTN/eqFGuIjU1Nug7q3Nr0cpU1PJ/s9Fy91+oc1r1xPINkvZJev2WYvPrpK53dFELGKGo/3a1diyUSp+WJr3xzpaae1ABoAa7BVZLi1XCto5ugbXro/vuqOMXjdOK6cUdVIzimRCfsiCoxaV7U6M+sHSCfn7qok7LS1kXq+NRynaZyltx57AaSWiBmHr821Xir3zZQTPLXQQARVJzLVfV0mKVkLjQtdZwpqH+vZp18Xt2LHcxUICTF++g865Pn4TpnL26zl9T7pu4REC4euG48hYkR5VyDts+5ipWHuarq1y3n71Yb23aWpZ9l7ueVzu+P6D21FxwlVBxT3/TaG3ppov33VHLpw4pd1FK7scn7aI+LTV7CKLMBvRqDjVGCcGmxJMMLJ3S9dxULefXejFpSPiEED2b6++cW2gA09zYoE1bt0VTGAA1r/7OshXo+F2r68l6VHbZocvQBVS4XFpVCrmfyfdmqFqeAnsJm4Hy/U4mDO6jRz6xt3p173qZqJbvGV1duv/0SLdXCWMbLz94hs7/2UNp3y+0jHecs1iPbPhPQdtIVSkt1JDeeGdzuYuAGlM3wdVH991R724pT7cJoNrlc3NSCTddlabavpOgwArVrW/P6uuCnq3ahJ1yZd+Z+c17OWZgL40ZyLyOtertMnWpRe2qmyvnCXXaOgREioetkajGrzG5sa26QsTase/MYbrpwecj2daajyyvmpvKbA29U4a2au2nVmrCRb9Ku879H92TbugIxgkNEau5bIEAope49uQSFHC96qpU38ltZy2OfJs9mhs7fj54zsjIt4/srjxiZ52+x/hItjWod3eNGtAz7fvVNg64qTHz7cyAXs3qlmWdcsg2fnF2yFY55I8xpIgaj3EAoMZMHho+wUFYVx6xk667d71OXrKDujdV3k1qvfr7xXsVZbvfPGZOxWSIrLLetJF58tOxKdZ2+PAtZS5JbavX4wvFQ3AFoChKecEqZYKISGQqboX+KsP69tAHl08sdzHqXuqhXqwxVGZWMTedYav3LR/YTY+/GG3iiXJqaKiQPwCAnOT9+NHMWszsHjP7u5k9YmafSLPee83s0fg61+Zf1HCq7R4LqAYdcx7lUMGqLXlDKZTjKxnet0fpdwqUwY7DW3XgTtXRZZV7lcrBlQpRK6Rvx7uSlrr7LEmzJa0ws12SVzCziZIulLTI3adJOquA/eWE+zogOsWuT1cdPae4O6gwGdMwR/xdJ4+VAqpRTV/Pa/l3qxI8CETU8u4W6LFH2G/GX3aL/0u9YzhR0lfc/dX4Z17Kd3+5l69UewJKw8w+J+k9kjZJekLSce7+WsB66yS9IWmrpC3u3l7KcuZjr2lDI9lOpQ9MbmqIPc/KOLCecxfQSb1fz/90/h56dwuTGBdLZV81UI0KGnNlZo2S7pM0QbEg6u6UVSbF1/uzpEZJH3f3WwO2c5KkkyRp9OjRhRSptp9wod7dIelCd99iZpcr1ip8fpp193D3f0ddgDq/xynY8qmDdfKSHXTK4mgyviV7b/tIvfnulsi3i8pSiXXw0Dkj9Z5Zw8tdjKqSy99xZP/0WR1ROO4bEbWCgit33ypptpn1k/QLM5vu7g+nbH+ipN0ljZT0x/g6r6Vs5ypJV0lSe3t7QdeOen/Chdrl7rcnvfyrpENKte/StwjV5tWuqbFBF66cWpRtf/aQWUXZLipLJc7V9LlDOfZQvSq9xwOqTyT5dOPB0u8krUh5a72kX7r7Znd/StLjigVbRceTCNS44yWlmzHTJd1uZvfFW4UDmdlJZrbGzNZs3Lgx1E4r/eEF9R617v277qCL992x3MUAagfXDUSskGyBbfEWK5lZD0nLJf0jZbUbJO0RX2eQYt0En8x3n0CtM7M7zezhgH/7J61zkaQtkn6YZjOL3H1nSSslnW5mgTPKuvtV7t7u7u1tbW1ZyhX/TO6/Ul1oLvLkpD98/3xdsHJKUfeB6tDc1KDjdx1X7mIgItzXA7WnkP4FwyRdEx931SDpOne/ycwukbTG3W+UdJukvczsUcUG15/n7i8XXGqU3JqPLFcjzQJF5+7LM71vZsdK2lfSMk+TF93dN8T/f8nMfiFpnqQ/RF3WYpo9qm+5ixDaH87bQ726Fzcj36IJg7RowqCi7iPhgpVTdPODz5dkX0A+bjh9UbmLULCqm5uvhnFrg6gVki3wQUk7BSy/OOlnl3RO/B+q2KDe3ctdhLpnZisUS2CxxN3fSrNOL0kN7v5G/Oe9JF1SwmIW7Pfn7a7RA3IbwF3O+5TRAwsbbD53bH/du+7ViEpTuFOWjNcpS6JPuAHkK/Xmd/aofuUpCGoSsRWiVty+LEAerjp6jr7AAOkgV0rqI+kOM3vAzL4uSWY23Mxuia8zRNKfzOzvku6RdHNQhs58hXnaetjcUQXtY8zAXnU178gV75vd6TXPs4HOaqmR5xP7TdPSKYPLXQwkqafrTb6WTMo8dACdVV7aIdS9qOY8qjXuPiHN8g2S9on//KSkyCPTXC4+nz5whi49YHrURUCOWro1aOYInvADleTYhWN17MKx+uUDz0nixr4S8BfIroEvKScEVwBCC/MAuaHB1FCmy1U1nv9Tb66i+h3+cenKiLYElBfxB4qJ4ys7HgLkpua6BdZQ7wGgYlT6afXgOSM1e1S/qsyixsD28jOzQ83sETPbZmbtGdY7O77ew2b2IzNriS8fZ2Z3m9m/zOwnZtZcutLXPqoIiol5rrLjG8pNzQVXAIqoQm9yBvRq1g2nL9Lwfj3KXZSCVehXXOselnSQMmTVNLMRkj4gqd3dp0tqlHRY/O3LJV3h7hMlvSrphOIWF7WCm9byo1EGmTTm0Sey5oIr6ggQPS4+qGXu/pi7Px5i1SZJPcysSVJPSRss1l9mqaTr4+tcI+mA4pS0PnH+AcqrnutgPr1Lai64AlA8TrsK6pS7Pyfp85KekfS8pNfd/XZJAyW95u5b4quulzQiaBtmdpKZrTGzNRs3bixFsWsC3QKBcqvf6Cqf8WY1F1xxDgail+iTzk0OqpWZ3RkfK5X6b/+Qn+8vaX9J4yQNl9TLzI5S8F1Hugm+r3L3dndvb2sjtXE941xaOeq5VSbhiylTgqAwNZstkLoCoBoM7BWboHvGiL566LnXy1ya2uXuywvcxHJJT7n7Rkkys59LWijph5L6mVlTvPVqpKQNBe4LSWrx5jfxO9Xi71ZtSGghTR3WmvF9jtPc1FzLFYDq9NmDZ2rOmP7lLkbJ9Whu1LrLVum4RWPLXRRk9oykXcysZ3yc1TJJj3msQ/5vJR0SX+9YSb8sdGc9ujVqMRN3SqrNVp4V04fq8Hmj9dF9dyx3UeoegUP276Cev6IBvXJP/lqzwVUNnouBskmceIt5k/PeuaP0s1MXFm8HQBpmdqCZrZe0QNLNZnZbfPlwM7tFktz9bsWSVtwv6SHFrp9XxTdxvqRzzGytYmOwvl1omR67dIW+d/y8QjeDCtW9qVGfOWiGBvXuXu6i1L1KCxw+f+ischehi3oOQC8/eEbOn6nZboEAolPH51XUAXf/haRfBCzfIGmfpNcfk/SxgPWelEQkVCT1fGOH4qu0CXLzyPxddPXcdbK1pVvOn6nZlqv6PQyA4iFbYG2ZMrRPuYuAElm9cKw+smpquYsBIIsKi/UkVWaZonTy4h0i3V7NBlcAolPrJ9Z69dNTFuj35+1e7mKgBD6+3zS9f7dobyBK5YPLJqqpEh/noyZU2pHVUIYLbqn2OLS1pUR7ys2SiMe3ElwBQNyUoX102u7jy1qGfCYszFeflm4aM7BXyfYH5GP3yYO19tP7ZF8RyAMPD7OL6ju6+QO7RrOhiG3LcNnN53evuTFXpbwxAepNrVevW89aXLZ9c4EHgNKrtDFXlVYeKboxVy3dGiPZTtS2RXxzQ8sVgBDikwiXuRS1rNYDVwBIZWYrzOxxM1trZhcEvN/dzH4Sf/9uMxub9N5MM7vLzB4xs4fMrDL7nOWo8kKrYPmkKK9UWwmuMqvEiB8AwuIcBqAemFmjpK9IWilpR0mHm1nqxF8nSHrV3SdIukLS5fHPNkn6gaRT3H2apN0lbS5R0YuqHJeArPsMeD+fnmKF/G6Hzxud/4ez2JapX2Aeai64AoBqRtdmAHVinqS17v6ku2+S9GNJ+6ess7+ka+I/Xy9pWXwS770kPejuf5ckd3/Z3beWqNxFVYlpz4NKlM+VqhJ/NynzmKt81FxwxY0JEL1STCJc72iwAlBnRkh6Nun1+viywHXcfYuk1xWbqHuSJDez28zsfjP7n3Q7MbOTzGyNma3ZuHFjpL9AMZQyMWZzUyIMyH2nUd8PlHNC7a0RR1c1l9CiA3cqQGSoTQAqwaIJA8tdBEQnTINIunWaJO0qaa6ktyT92szuc/dfd1nZ/SpJV0lSe3t7xT8iLOXt6zePadd9617RuEGlyRqb6Xfr37Ob/v3muxk+Xbw/3agBPTK8m/sfpOZargAUU8VflwDUqH9cukLXHDev3MVAdNZLGpX0eqSkDenWiY+z6ivplfjy37v7v939LUm3SNq56CUuidJFV0smtemcvSarMUtz2Zp1r3ZZVoyeYrtNHJT2vW3bIt9dh2nD++quC5dGtr3aDa7ovwREhiQLAMqtpVujmhpr97alDt0raaKZjTOzZkmHSboxZZ0bJR0b//kQSb/x2F39bZJmmlnPeNC1RNKjxSzsiH499L72UdlXLOoiGC8AACAASURBVFAlXm5f+e+mkuzn6gwPT6JOl56qf8/osh9ylgIAAEBJxcdQnaFYoPSYpOvc/REzu8TM9ouv9m1JA81sraRzJF0Q/+yrkv5XsQDtAUn3u/vNxSzvny9YqkUZWlai0lCB0dWWgGajvBJaZPnVMrWgRZ0uvZgYcwUgtCo6t1UtvmIA9cLdb1GsS1/ysouTfn5H0qFpPvsDxdKxR27l9KH61cMvBO2zGLvrpBLvXqPOphck2y6q6f6DlisAWSVO9lV0bqs6lZqiFgDqTbouaKmLJwzurTEDe3a8vvbE+QXvO2zbwH6zhhe8r0J0b2rM+TNhrnNfPXJnXX3c3C7Lo87ol6qlW6P+7/CdItkWwRWArGgILj4ndAWAsvifFZM7vU53H598nl69cKzuPGeJhvVt6Vg2b+yAgssS9no7oFezvnH0nJy3f+Ju43L+TKqvH7Wz2vrknjo9zO+2z4xh2n3y4E5Bq1SaboFRBaw1F1x1iw927dejW5lLAgC5I44FgNI6bfcJnV4fMX904HrJ9/dBXQSzZd0LI6h1p0e34FaifMZnXbRqx5w/kxp8rpg+rOjzcd121uLOCwqMreaPG6D2Mf0L20hINRdcTRveqk/sN01XvG92uYsC1Bwm6S4+vmEAKK8lE9u0bMrgLss7BVfx//efvX3e40gy6yZtom+Pblp32Sr1aQlOkZAa4By4U+oczNHYfVLX7yKfXzXTR1Lfa0kJKEcO6KGjdhmtkxfvkPuOJU0c0lun7j4+r8/mquaCKzPTsQvHakCv6FIqAvUucRLlxr94GHMFAJWhocH0v++drV+evqjT8uRrYCLQOmxutOnZk1ujsrUOpbZcFesqEjQGrRxZDT95wAy159n1Mt/y5vOxmguuAESPG38AQD3p27ObZo3q12lZcpCRGH+V2lr15wtyn4z2oKQWp+StZWoJM4sFgZ0X5rzrUDZv7ZqKPZdWui++b7ZuPGNRYS178a9+S0BZwjCVbvw4wRWA0OgVWDzNTbHTcbq+9QCA4vnKETsHLv/A0gn67CEzYy86jbkK3s6Ifj1y3veBO4/QT07aRb2aGzVr5PaALhE79WhON+aq8+tiPQjdEpDhI3Xf3zqmPe3nF04YqJkj+xVUukQJNueZNTCSLpshEVwByI6Gq6Lbe9pQnb18kj68amq5i1J3zOxQM3vEzLaZWdo7BDM7O77ew2b2IzNriS+/2syeMrMH4v8Y9AtUmVUzhwUuP2evyXpve6zrX3LL1W4hJhRed9mqUPtuMNP8HQbqkUtWqG/P7QnZEgHBxfsGJ6FIDaaS44fWgHFaF6ycEqo8qYJarlK72S2bOlg7je7XZb2gdZN9/ajcMh4mWq4aG0zXnbwg6/qThvTuKENLHunj80FwBQAVoLHB9MHlE9XaQqbTMnhY0kGS/pBuBTMbIekDktrdfbqkRkmHJa1ynrvPjv97oKilBVBW+88erhXTg4OxfKQLPRLLl00dojvPWawTdt2eRr19zICAlqvt/vrhZRrUO5Z/4OYP7KrHP7lCpyzJL6HDhLbeXZZ12beZ/u+w4HmiesZb3oJirLANSomEWoltvX/XcZo3LvP4q349u+nQOaM6yrtg/ECdvXxSuB0WgOAKQGj0CkQtcvfH3P3xEKs2SephZk2SekraUNySAagkiWtg1N23U7usXbRPrAdDcovPhMF99NGkFqxVM4d1HXOVpLmxoSPjXmtLt6yT/n5ndfpufYNbW7q0wgV1QWxq7Lrsng8vU8/mWCtaUNe8sB1jEt/53tOG6rKDZuicvWJB0pFp0uYntp1obWxoMJnFHmKuu2xVXt03wyK4ApBV4uRHKnbUK3d/TtLnJT0j6XlJr7v77UmrfMrMHjSzK8ws99k1AVS8xI161MN3Urc3saMrW+bPdWtM3y3QzDrm3doaYpzS0ilDOk2InE3QdxA0z9fg1szbDDMW6ty9Jum0PSZ0rH/YvNFZg0UpFpwOie8/NZhaPCnWrXNIa9fT9fi2XtvLl3UvAfvN4zOoYZ8+cIa+mWFQIgBUIzO7Mz5WKvXf/iE/31/S/pLGSRouqZeZHRV/+0JJUyTNlTRA0vlptnGSma0xszUbN24s+HcCUFqJ54tRJUfYIX4Tn7q1xPaz7adbY+fb+OS5oRpMaox/fmsRHowGjaNqasg9rAgzGfEZSyd2mfcqDLNYF85vH9uuo3cZ0+m9U5fEgrVEMqmEtZ9aqdvPXtLxOp9vLu/gysxazOweM/t7fIDvJzKse4iZeaaBwqgMR8wfrT13HFLuYgBApNx9ubtPD/j3y5CbWC7pKXff6O6bJf1c0sL4tp/3mHclfVfSvDRluMrd2929va2tLYpfC0AJJW60cwmtUluXkiVaX1K79yVayJ577e0s2+58G3/e3pM7fjaz7XNUhgyuvrN6ro6cP1rHLhgT+P6qmcM6uuEFxVGpLVc3f2DXtPv61Qd309nLJ2lk/56hyhZWctfJ2HdgWjZ1SJfveGT/Hjpy/uguCTWaGhsCW+ByETzlczjvSlrq7m+aWTdJfzKzX7n7X5NXMrM+ig0CvruAfQEoo1KmMAUq1DOSdjGznpLelrRM0hpJMrNh7v68xSrKAYolyABQa/LoFtitsUGbt25Ns7n49lKWTx7SJ+S2t39y3rgB6pOSECnRuhQ2e/nUYa361IEz5O665q6nu7yfnK4+qOWqT/fOYcW04X0D9zOod3dNHdaqqcNa9c8X3whXuDSmDO38XZ2w6zhdetOj8TKm/1xDg+lTB87Iuv2SdguMP6V7M/6yW/xf0J/vUkmflfROvvsCUF6EVqhlZnagma2XtEDSzWZ2W3z5cDO7RZLc/W5J10u6X9JDil0/r4pv4odm9lB8+SBJnyzxrwCgBPaaNlQDejXr2AVjs66b6IaW3LrUPqa/pK7jfFIfYPbvGcvy1yvN/FYJveLBzJDW7vru6rmSpHGDto8XSgRAqWOuPnnA9IxpzMM8UJ0UEAAmtw6lS0P/lSN21g2nL+yyPLUVTpKuOnqOfnZq5nTrR+0yRjeesajTshtOX6QDZg/X90+Yn/GzmVzxvlmSpLEDe2VZs6tCWq5kZo2S7pM0QdJX4hef5Pd3kjTK3W8ys3MzbOckSSdJ0ujR6bN+ACgv8lmgFrn7LyT9ImD5Bkn7JL3+mKSPBay3tKgFBFAymTIBDmlt0f0f3bPL8jvPWax3Nm+fCyo5sEgEDf17dtPnD52l0QN66o13tujR5/+jh597XZ+65bGOlOkJLd0adNyisYFzafVsbtS04a2SpGF9e+grR+ysheMHdgRaN525q97ZHGspSwQ721Iu3kftEtztLxdnLZ+o9jH9deoP78/pc6nziQ3v10MzR/bV2Xt2TZG+17ShWbdnZpo5svP8WrNH9dMX06SFD+vAnUbqwJ1G5vXZgoIrd98qabaZ9ZP0CzOb7u4PS5KZNUi6QtLqENu5SvEngO3t7dy+AQHM7HOS3iNpk6QnJB3n7q8FrNdP0rckTVesNfl4d7+rkH239oh1NRjeL3wmIQAAqsnVx83VhMFd53TKZsLg9N34Prh8oj56w8O668JlHUkZ+vbspgXjB2r+uAE6YKcRauvTtSXrY++ZFri9Ry9Z0el1arDSq3tTR6B11vKJOvn792lMHq0v2fRp6aaVM4ZpxbShuvWRFzqWn7PnpMAJjNPp3b1JN56RfmxWWNe+f75eeWtTwduJgkWVWtnMPibpv+7++fjrvordACa6Dg6V9Iqk/dx9TbrttLe3+5o1ad9Gifz92dfUs7lRE0P2+61UZnafu9dEIhUz20vSb9x9i5ldLknu3iUrmZldI+mP7v4tM2uW1DMoCEsWpt7d/ODzWjplsHpk6aYA1FK9K5ZCr3UPrX9dzU0Nmjy0us/RiAZ1LhzuMcO756lXNLS1RaMHZk44sWXrNm3auq1jLqt6kq7e5f1NmFmbpM3u/pqZ9VAsk9Lliffd/XXF+p4n1v+dpHMzBVaoHLNG9cu+EkoqZU6dv0o6JHUdM2uVtFjxFmN336RYS1fBUp+OASifGSODB4oDQBTmjRsQar2mxgY1BYyXqmeFfBvDJP3WzB6UdK+kO+Jjqy4xs/2iKR6ANI6X9KuA5TtI2ijpu2b2NzP7lpkF9gdgzh0AAIBo5d1y5e4PSuoyWszdL06z/u757guoF2Z2p2JdaFNdlJiPx8wukrRF0g8D1muStLOkM939bjP7kqQLJH00dUXGOgIAAESr/jpIAhXM3Zdnet/MjpW0r6RlHjxgcr2k9UmZO69XLLgCAABAkdFJEqgSZrZC0vmKJYV5K2gdd39B0rNmlpimfZmkR0tURAAAgLpGcAVUjysl9ZF0h5k9YGZflzpPdBp3pmKTmj4oabakT5e+qAAAAPWHboFAlXD3CWmWp050+oAkUvICAACUGC1XAAAAABABgisAAAAAiADBFQAAAABEwIKzOZePmW2U9HS5y4GaMcbd28pdiEpHvUPEqHdZUOcQMepcCNQ7RCyw3lVccAUAAAAA1YhugQAAAAAQAYIrAAAAAIgAwRUAAAAARIDgCgAAAAAiQHAFAAAAABEguAIAAACACBBcAQAAAEAECK4AAAAAIAIEVwAAAAAQAYIrAAAAAIgAwRUAAAAARIDgCgAAAAAiQHAFAAAAABEguAIAAACACBBcAQAAAEAECK4AAAAAIAIEVwAAAAAQAYIrAAAAAIgAwRUAAAAARIDgCgAAAAAiQHAFAAAAABEguAIAAACACBBcAQAAAEAECK4AAAAAIAIEVwAAAAAQAYIrAAAAAIgAwRUAAAAARIDgCgAAAAAiQHAFAAAAABEguAIAAACACBBcAQAAAEAECK4AAAAAIAIEVwAAAAAQAYIrAAAAAIgAwRUAAAAARIDgCgAAAAAiQHAFAAAAABEguAIAAACACBBcAQAAAEAECK4AAAAAIAIEVwAAAAAQAYIrAAAAAIgAwRUAAAAARIDgCgAAAAAiQHAFAAAAABEguAIAAACACBBcAQAAAEAECK4AAAAAIAIEVwAAAAAQAYIrAAAAAIgAwRUAAAAARIDgCgAAAAAiQHAFAAAAABEguAIAAACACBBcAQAAAEAECK4AAAAAIAIEVwAAAAAQAYIrAAAAAIgAwRUAAAAARIDgCgAAAAAiQHAFAAAAABEguAIAAACACBBcAQAAAEAECK4AAAAAIAIEVwAAAAAQAYIrAAAAAIgAwRUAAAAARIDgCgAAAAAiQHAFAAAAABEguAIAAACACBBcAQAAAEAECK4AAAAAIAIEVwAAAAAQAYIrAAAAAIgAwRUAAAAARIDgCgAAAAAiQHAFAAAAABEguAIAAACACBBcAQAAAEAECK4AAAAAIAIEVwAAAAAQAYIrAAAAAIgAwRUAAAAARIDgCgAAAAAiQHAFAAAAABEguAIAAACACBBcAQAAAEAECK4AAAAAIAIEVwAAAAAQAYIrAAAAAIgAwRUAAAAARIDgCgAAAAAiQHAFAAAAABEguAIAAACACBBcAQAAAEAECK4AAAAAIAIEVwAAAAAQAYIrAAAAAIgAwRUAAAAARIDgCgAAAAAiQHAFAAAAABEguAIAAACACBBcAQAAAEAECK4AAAAAIAIEVwAAAAAQAYIrAAAAAIgAwRUAAAAARIDgCgAAAAAiQHAFAAAAABEguAIAAACACBBcAQAAAEAECK4AAAAAIAIEVwAAAAAQAYIrAAAAAIgAwRUAAAAARIDgCgAAAAAiQHAFAAAAABEguArJzIaa2UVl2O/vzKwpzXurzez9RdrvcDO738zeSew/vr/HzWxJ/PV5ZvYnM/uhmXUzs95m9lcz+0ExyoT6Uod1br6Z/cXM/mhmV8SX7W5mT5vZ++Kvj4yvc5OZtcaX3WRmfypGmVBf6rDOTU+qc9+1GK5zKKl6q3dJ+zgnce2qtXpHcBWSu7/g7p8qdzlK6BVJyyT9NWX559z992bWJmkPd99V0oOSDnD3NyUdVuJyokbVYZ17WtJSd99N0mAzmxFf/n13/4mZdZN0iqTFkr4v6WRJcvd9y1Ja1Jw6rHOPu/vCeJ2TpPb4/1znUDJ1WO9kZt0lzUpZXDP1juAqAzNbaGZ3m9lvzOz4RMRsZqfEI+jLzex38WW/M7NPmdkaMzvRzH5gZn83s73j719oZr+Pb2+nNPtLu46ZfdzMrjazO83sO0lvLTOzW+L/zMxmx7fxVzP7cJr9rDaz68zsNjP7pZk1p67j7u+4+6sZvp55kn4X//lOSbtkWBcIpc7r3Avu/k785RZJW1NWmSTpIXffIuocIlLndW5z0st3JT2bsgrXORRFPde7uPdLuibNe1Vf7wiuMttH0vnuvlTSbyXJYk2oqyUtkvTzlPWvjy+/TNKHJK2UdHr8vS+5+xJJR0o6N83+sq3zL3dfLuldM0scbC+5+z6SnpM0U9LjknZ3910k7WlmPdLs63V331vSXyQdlGadTPpJ+k9iW5L657ENIFXd1zkzmylpkLs/mvIWdQ7FUNd1zsz2M7OHJQ2W9HLK29Q5FEvd1juL9cJY4u6/SfP5qq93gX0t0eGrkj5iZidI+nJ82SBJz7j7VjN7IGX9h919s5n9w91flCQzSxwUR5vZkZK2SfI0+8u2zt/i/z8gaUJin/H/n1PsgBwn6Qtm1lPSZMUuGE9n2dbcNOXJ5DVJI+I/t8ZfA4Wq6zpnZgMkXSnpvQFvv6ZYXZOoc4hOXdc5d79R0o1m9mVJqV1suc6hWOq53h0t6do05ZRqoN7RcpXZq+5+mqTzJX0ivuzfkkaZWYNikXwyT/lfkiz+/2mSdpd0YtKyVNnWmZX0/xNp9nWqpMvjTyjWZthX0LZyca+kJfGfl6vr2CwgH3Vb5+JPLX8g6Tx3fyHg8/+UNN3MGkWdQ3Tquc51T3r5H0lvp6zCdQ7FUrf1TrHA7FQzu1XSNDM7M+X9qq93tFxldrKZHSSpt6SfSJrh7lvM7BrFmjvvkrQ50waS3CPpD/F/+a4z1cx+LelZd7/LzCYHrHOzpCvN7FFJmzLsa6CZ3S7pHUmHpr4Zb7b9lWKV47bU/rXu/pKZ/cFimV6ekfTFDPsCwqrbOhdfNlfS5WYmSRcmvxl/avlNSX+U9KqkIzLsCwirnuvcCjM7J/7zvyTdLumYxJtc51BEdVvv3P38xM9m9id3/7KZrU56v+rrnbmna0FEOmbWFK8E8yUd7+4nl2CfH5f0J3e/M4JtrZbU5O7fyvFzh0i6QNKH3P33Ae/3lnSrpHvd/exCywkk1HGd20XS1yV9xt1/kmadmyS97e5BN49AXuq4znGdQ9lQ72qj3lVccDVo0CAfO3ZsuYuR0YsvvqjXXntN7q6xY8eqpaUl720k9OvXT0OGDEm7/oYNG9S7d2+1tramXSfIO++8o6ef3t4ltqGhQf37x7rpDho0SJK0detWrV27ttPnJkyYoMbGxpz2VYnuu+++f7t7W7nLUekqvd5R56oL9S476lxX1Ln8UefCod51Rb3LX7p6V3HdAseOHas1a9aUuxioEWYWNNgSKah3iBL1LjvqHKJEnQuHeocopat3JLQAAAAAgAgQXAEAAABABAiuAAAAACACBFcAAAAAEAGCKwAAAACIAMEVAAAAAESA4AoAAAAAIkBwBQAAAAARILgCAAAAgAgQXKU4/dr79dEbHi53MQAglCt/8y8d8rW/lLsYQFH8v79v0C6f/rW2bN1W7qIAqHE3/O05LfzMr7Vtmxe0naaIylMzbn7weUnSpQdML3NJACC7z9/+z3IXASiaj9zwsF5/e7PefHeL+vVsLndxANSw83/2oN7dsk2btm5TS0Nj3tsJ1XJlZivM7HEzW2tmFwS8v9jM7jezLWZ2SMp7o83sdjN7zMweNbOxeZcWAADUDbPY/wU+SAaAkskaXJlZo6SvSFopaUdJh5vZjimrPSNptaRrAzbxPUmfc/epkuZJeqmQAgMAgPoQj63kTnQF1LKfrnlWL7/5brmLIUkq9HQTpuVqnqS17v6ku2+S9GNJ+3cuhK9z9wcldeoUHQ/Cmtz9jvh6b7r7W4UVGQAA1IOGeNMVoRVQu5595S2dd/2DOvWH95e7KJEIE1yNkPRs0uv18WVhTJL0mpn93Mz+Zmafi7eEdWJmJ5nZGjNbs3HjxpCbBgAAtWx7t0DCK6BWbYonrNn4RmW0XBUqTHBlAcvCnuWaJO0m6VxJcyXtoFj3wc4bc7/K3dvdvb2trS3kpgEAQG2L34IQWwE1KyjQKAeLqCBhgqv1kkYlvR4paUPI7a+X9Ld4l8Itkm6QtHNuRQQAAPWogdgKqBu1MrYyTHB1r6SJZjbOzJolHSbpxpDbv1dSfzNLNEctlfRo7sUEAAD1hm6BQO2zqJqMKkTW4Cre4nSGpNskPSbpOnd/xMwuMbP9JMnM5prZekmHSvqGmT0S/+xWxboE/trMHlKs5e+bxflVgNrB9AcAkJTQgtgKqHmVUs29wJKEmkTY3W+RdEvKsouTfr5Xse6CQZ+9Q9LMAsoI1JWk6Q/2VKxr7b1mdqO7J7f6JqY/ODdgE9+T9Cl3v8PMeisliycAVIuOVOxlLQUASbr/mVfV3Nig6SP6lrsoFS1UcAWgpDqmP5AkM0tMf9ARXLn7uvh7Wac/KFGZASBy1tFyRXgFlNtBX/2LJGndZavKXJLKFmbMFYDSKvr0BwBQDRJDMYitgNpVKSOuLKKSEFwBlafo0x9IzC8HoPKR0AKoH5VSzQstB8EVUHlKMv0B88sBqHQktABqX0cLdY2MriS4QlX754tv6Mu//le5ixE1pj8AAJHQAkDpFZoZnuAKVe3gr/1FX7jjn3p709ZyFyUyTH8AADEktABQaoWebsgWiKq2aUssWV6NzT/H9AcAoOQxV+UtB4DiiSqRRKWg5QoAAFSkjm6BtFwBNa9SqnmhxSC4AgAAFamjW2CZywGgeCql91FU5SC4Qk2olKcdAIDobNka6/r91L//W+aSACi2SrmXK7SlnOAKAABUpFEDekranpIdACodwRUAAKhI3ZtitymMuQJQKoy5AgAANYoxVwCqC8EVAACSzGyFmT1uZmvN7IKA9xeb2f1mtsXMDgl4v9XMnjOzK0tT4vpBwxVQuyqt12+h5xuCK1S1SquQAKqTmTVK+oqklZJ2lHS4me2YstozklZLujbNZi6V9PtilREAalm5u/9GdUtJcAUAgDRP0lp3f9LdN0n6saT9k1dw93Xu/qCkbakfNrM5koZIur0Uha0/NF0Btcpq7Ek5wRUAANIISc8mvV4fX5aVmTVI+oKk87Ksd5KZrTGzNRs3bsy7oPUkcc9Ft0AAJUO3wMJs2+Za8rnf6oa/PVfuoqAAzlNNAIUJenQa9sRymqRb3P3ZTCu5+1Xu3u7u7W1tbTkXEABQ+eo+uNq8bZuefvkt/c/1D5a7KCgATzUBFGi9pFFJr0dK2hDyswsknWFm6yR9XtIxZnZZtMWrb5ziaxNJZJCsUup5oQ/s6z64qmazPnG79r7iD+UuBgDUgnslTTSzcWbWLOkwSTeG+aC7H+nuo919rKRzJX3P3bvcKCJ3ieZEHqDVHpLIIKHW6jnBVRV7/e3NevzFN8pdjLIy5kABEAF33yLpDEm3SXpM0nXu/oiZXWJm+0mSmc01s/WSDpX0DTN7pHwlBqoeSWQgqfIyPxca5DVFU4zqVStRMgCgMO5+i6RbUpZdnPTzvYp1F8y0jaslXV2E4tU1xtXWpKAkMvPDfDApiczRkpZlWfckSSdJ0ujRo/MqKEqj3PU8qqyFtFyhJpR7bgQAQPTIFljTip5ERiKRDHJX6Ommrluutm5zXXv3M+UuBgAAKNBvH39Jbb27a/qIvuUuCsIpNInMbmZ2mqTekprN7E3GOqIS1HVwdf19z+qSmx4tdzEQAR5qAkDtCnOOP+6790qS1l22qriFQVQ6kshIek6xJDJHhPmgux+Z+NnMVktqJ7CqXhbYiFk+hfaGqutugf95e8v2F5X1dwUAAHF0/a49JJFBqnJX86hCgbpuueqE83ZVK3eFBABEr9KeaCNaJJEpnedff1t3PPqijlkwttxF6aJjbGV5ixHZ/gmuAAAAgBp23Hfv1T9eeEMrpg3V4NaWchenk0p7hFJokFXX3QJRQ8r9uAMAUDT0TgAK8+pbmyRJ2yqwLlVKkaIK8giuEiotbEYolTbxHAAgOtu7C1XK7ReAWlfow5y6Dq463Zhz3q5qXHgBAAAyq+T7pVppoa7r4KpW/ogAANQyrtdAYUgOE16hAWio4MrMVpjZ42a21sy6zCNgZovN7H4z22JmhwS832pmz5nZlQWVtpg45qoaF14AqF2c41GJfvf4S3pkw+vlLkYoldpitW2ba1tHBa/MMuYqa7ZAM2uU9BVJeyo2m/a9ZnajuyfPvvuMpNWSzk2zmUsl/b6wokaP8ToAAFQurtOoZKurcOLqSmvBWvGlP+ifL74pqYIeopRgzNU8SWvd/Ul33yTpx5L271QG93Xu/qCkbakfNrM5koZIur2wogLpVUp9rCfurrUvvVHuYgCoA5zjgWhUWgtWIrCqCBHFnWGCqxGSnk16vT6+LCsza5D0BUnnZVnvJDNbY2ZrNm7cGGbTAMrsR/c8q+X/+wf95Yl/l7soAGqcV8wjbaA6VVqLVSUrxTxXQX+NsPs9TdIt7v5sppXc/Sp3b3f39ra2tpCbRrJ/v/mufrom49dc07jwlt5Dz8X6mT/17/+WuSQAAADhvb1pq77756e0rQgTf2Udc6VYS9WopNcjJW0Iuf0FknYzs9Mk9ZbUbGZvunuXpBgozCnfv09rnn5VCycM0oh+PcpdnJLhOQwA1D4enwGFqbTugEFKWcLLb/2Hrv7LOg3r26IV04dFuu0wLVf3SppoZuPMrFnSYZJuDLNxdz/S3Ue7+1jFkl18j8CqOF56411JueeTOgAAIABJREFU0patXYa91YxX/rtJtz78QuB7lX/KyE1dZOgEgCw6ujLV2kkeKII1617R2Atu1tMvZ+5R8quHntcVd/yzRKWqTK+/vVmS9NamrV3eK/okwu6+RdIZkm6T9Jik69z9ETO7xMz2kyQzm2tm6yUdKukbZvZIYcVCvmq5d9wJ19yrU35wn17976ZyF6WokjJ0rpS0o6TDzWzHlNUSGTqvTbOZiszQWQs2b92mvz75crmLAQBAJ9fft16S9Jcnul6jksdcnfrD+/WlX/+rZOWqZMW4bw41z5W73+Luk9x9vLt/Kr7sYne/Mf7zve4+0t17uftAd58WsI2r3f2MaIuPevLMy29JkrYG1IQaCyrJ0FnBPn/74zrsqr/qb8+8Wu6iAHUj0aXp9kde0PpX3ypzacJ79pW39O6Wrk/GAVSukkwiDFSCxCRzdTDOqugZOpG/tfG0sS+/WdstqEBFSPQKjN/rnPT9+7Tq//5UvvLk4J3NW7XbZ3+rD13393IX5f+zd95xVlTn//+cLfTeVDpSlKKoIHbsiiWa2DWxJxoTo1ETf3yjwd57S9TYe40RBQWlSe+9L7CwS9sFlqVt3/P74965O3fulDMzZ+p93q8XL/beOzPnzMxpz3OeQljQ/58/4uUY7OSIKJrDrIwOOjiZrPVlbISrol0H8NB3yx1H/Yj6gp35YJa+ced+PP/TmsAav1Iq08kqGQVHTRt4HqEToBQIBEFEB/UAqPhKhJ2q2oRhwZTVNL6GnYqaOjyf5T5IYSAsKznPfa6iwp2fLcS70wuxuHi3o/PD8kKd4odweON7c/HyhLUoLqvwobRMlMYedUFYALcROm9njBUCeBbAdYyxJ/UOpBQIBEGEnUiP9ymNYKC1ILIIHd2zo2OyCS+eh0go9khQF3XpKAJU1wYbiVDZMdPtCPF6/6kInQA2IxGh8xqREznnv1X+ZozdAGAoReiUS7yaGkFEgzCbMhFEWIh6Pwmi/npl+pFEmCBCQYMSMN5qF4rQSRAEkU7MTL8JIjDMBJjaunoMffRnfLdY1FgmXui5nTghNjtXbsXduCzXg3YG9BTFLFBHJRC3u+acjwUwVvPdKNXfc5EwFzS7xnsA3vOgegRBEIQFikAYl/UFEX7MZAMRuaG8ogY79lXhgdHL8avBneVVLGK4XUvHbufKqdQZ9cW5LGk7zOhFC8yG+yYIgsgmTnpyIn7//ty076KoN0z5CdM8RUSMWCvqk3jZK+Ozc5UkGxoEkQm9dsILausSfoZ5ubHTQxFEaNm8uwKbdycCJymCSZSHeJKtCL9wuxYKWhEQljU8RQtUcNkgaOwLP+HockQ2cdJTEzHggXG6v9GCiSCAKWtK0ecfY7GnMhrh0f2E5iwiqgTVdv0sd9LqEs+uHR/hKiTSbtDE+SmYvWJydia8YPueqsCjZBJEWHlw9HJc/84c1NZzrNm219vCaI4nCEtI6SdO2YGEQsiLkSU+wlWSoLc0g8LPuw5qjqunyZUgCCI0vDej0PMylLktyqN/dq5KiCCgZVI4iJ1wRcQfvbGDBhSCIAgiTITFf4Qg1Ji1ymxUBHhxz7ETrmgw06d0bxUueHkqtiSdhN0Q1OYgvdlwct83y1C2vzroahAEIZm6eo4HRy9PBZcIkihO7ancjFlqUUP4j6ymFlh/CyKJsN53FNCCUGPUIL6aX4zlW/bg/ZmFflZHKmaCcwTn3VixqHh30FUgCEIyCzaV4b0Zhbjr80WB1UFZLEZZcUqiFeEXIt2E2qMxsoTT2AlXWash8vG2g5rjIjy3EoRjxi3fhg079gddDSILUcbc+vrgB9/ga0AQhNeY9fO6eo43f1mHypo6H+pBSYQJnwhablWaup6QFWWtJhEt/G5rt344H6c/O9nXMgnCLjQCZ0LTEuE3Ruu0J39Yha3llQAsfK5CvD/x3wXFeHzsKrw0YW3QVbEkq4UrGbtcJXsrQ2GPLoyLwT7oiUJvURvicYAgCIKQRNDzjxMU7XeYF6xEdDjj2cn4aNZG02OM+snrU9bZKiuMCuuK5I7Vvspaz8sinysXyGg8wx6bgJOenGh53PSCHeg5cgxK91a5LtMc7ztEGANahHAcIAgiYjDGRjDGVjPGChhjI3V+H84YW8AYq2WMXab6/ijG2EzG2HLG2BLG2JX+1twbwiAUxCEUO6kBCRms37Ef9/9vmdCxUW1xXgp1X88vxn8XFJseI+u5ZbVwpcbrSeSdaRsAAIuKAnb8l3Cf5HNFJKAXQsQHxlgugNcAnAdgAICrGWMDNIdtAnADgE803x8AcB3nfCCAEQBeZIy18bbGRJipqq2jOYsAAGzaeQA79nmtWE8nzk3PqT/UPV8uxt1fLBYswx15Ls+PNGqzQK8HwTBoAN0Sh3sgCIIwYBiAAs75egBgjH0G4GIAK5QDOOeFyd/q1Sdyzteo/t7CGCsB0BEAhdGUhFajPX9jGYb0aBtQbcypqK5D/1E/4tJjugKguTPbGf7MJDAGbHjigqCrYgkLeM8rLkIh7Vz5jFdbnsLdIaQ+V5xzFO06IHiwd/Ug7MB0/soewr5gWr1tLxZsKgu6GlGiC4Ai1efi5He2YIwNA9AIQIaTA2PsFsbYPMbYvNLSUscVzSaMfKNXbt3jc03E2VeV8An52sIEicgelPUT5xzTC3Z4HoHT7fQU5mWWXQHw+yVb8PxPa6wPlEhshKswN4QE/qzE/DBD8GJR+dHsTTjl6UlY7NBskswviCixuGg31pXu87SMc1/8BZf8a4anZcQMvZHN1sjCGDsEwIcAbuSc12t/55y/yTkfyjkf2rFjR4fV9B+RhxByXQMRUrLNz/HHZdvw27dm40OLwBRusTNw7T5QjWWbyxMfgo4K7cFa7vZPFuJlmxEG3W6ExEa4cktNXcY86AmBywAh9bmau2EXAFA+HyIruPi16TjzuSlBV4NIpxhAN9XnrgC2iJ7MGGsFYAyA+znnsyTXLRDsTBdez23aeUd2eVPXlqK4TNB6woTKmjpc8cbMtO9I8NQnG/0clejSG3e6b2uyuPTfM3DhK9MSHwJfpFrjNgeVH8RGuHI7eHmdIzHsJkQi+HEPTsuIQmeLH/TMg2bW+p1BVyFOzAXQlzHWizHWCMBVAEaLnJg8/hsAH3DOv/SwjllHQ7RAOePN2u178ej3KzI009e+PQenPTPZ9fWXFJdnKAnjMP97RMrPkXNeDUDxc0zBOS/knC8BkOHnyDlfm/x7CwDFz5GAvTXxutLsVmrr7VK5HW1iI1xFBa/M1/Ts0uvqOf41uQD7q1Q5AUK6HrZTrbRJliatWLNx535MWlUSdDVCy/jl24OuQmzgnNcCuB3AOAArAXzBOV/OGHuYMXYRADDGjmWMFQO4HMAbjLHlydOvADAcwA2MsUXJf0cFcBuEBTe8OxdvTduALcmEqmpq6zmqausCqFXW4rmfY1gJrcAdtFmgwGow6KAbIsQmWqAXMsPYpVsxqHNrdG/fzPW1VHEJXV/LDPXVxyzdiqd/XI3t5ZU4pE1T99cOqWAGhLtu8cX7Ae7UpCa58MnwR1kKAtqxlQvnfCyAsZrvRqn+nouEuaD2vI8AfOR5BQMiDAlFM6rgsE71yfOMRq8PZ27E70851NG1jYjCYjAgZPo5Xq/n55g85hYAtwBA9+7d7dYxO+HAl/OKsKS4HI/8elDQtUlD9ryntzlBSYQ1yBzC/vTxApz74i9SrhWElqIymc16fzXl2yC8xSiiV9ypr+f4cdnWwBaf1K+JbCFDtnJ6neSJ9Qadp6rWH/9rAoBPfo5RDSTjFaLz1d+/WuJ54A0tZlXzapWhfh6y1jKxE65krzUqauSaCHixGCooEY86tmLrHschQL1cP6sb9+Ki3bbrSGvMYAmDZtsv1Hf68eyN+ONHC/DlPAq5TBCekJx3MgJaOBxytu1JmAO+PkXfgsyLeS5LdU8ihMLPMZvmr6hQurcKd3+xKLVJ4D8ULRBA4GailnhpFrD7QHWD069Fe5i6dgdem1TgWV2colR7wcYyXPzadPzbYOIjwgRNSMpCrWRvpv8GQUSdOAsFq7buDboKWQ/5OTrDrcARBUuTJ35Yif8u2Izvl2xNfadecVRU1+GCl6dikcP0PV4TG+HKyTIviObl1XLUqq+of1+q5DMIIYqTsVmCSD0BkjRPwRKFwdovaurq8c3CYmqTBCERv/wLvVCE0uhoDOd8LOe8H+e8N+f8seR3ozjno5N/z+Wcd+WcN+ect0+GXgfn/CPOeT7n/CjVv0XO6iDvfvzg8tdnWh9kgsjcFPZHsnxLOZZv2YNHvl/hyfXJ50pDmPJyqAl67RmVwaMh7C4RfmjJoNev3piyDnd9vhijFwu7DhBEqAlyPFaEnUyzwOjMEqR8IhRkNFtPFeQBdyuz4nWjn3CO+nqOVyeuRXlFjevyZfXU2AlXYcfX+SA6c0+qrs7zXBFEOCjdWwUAKNtf7XlZUVpgEgRBEAk8C84Q9dWQYPXVa8VJq0vw7Pg1eOi75cYnyC3eEiHhijE2gjG2mjFWwBgbqfP7cMbYAsZYLWPsMtX3RzHGZjLGljPGljDGrpRUb0PsPBg/dUleKq7U1xbKERBiJVrKJCPi4wMRfyi8MkFEF6N50Gx+/O+CYuzYV+VNhYjA8Gq5MWl1iamLAyGH6mSEzwNVznzR9N6/50mEGWO5AF4DcB6AAQCuZowN0By2CcANAD7RfH8AwHVJG9kRAF5kjLVxWedI45VWQXehp/pKPWGEeVHIUrKV8XNS/9KueSMAQOM82oT1H5KACSJOlFfUYGea8BD8XJGaEzS7tG5HH7ubvtvKK3H3F4tx64fzXZbcwOtT1qHnyDGpzxNWbsdPKygxeFy48d25OO+lqb6WKWt9F0WrCKfra8XaBPDXLHAYgALO+XrOeTWAzwBcrD6Ac17IOV8CoF7z/RrO+drk31sAlADwNMlA8FOBPkELNDL7iRcConJNu7tqJ/fpgA4tGqFrW/eJngnCLVW1dXh/pr95QQhCFsc++jOGPPpz0NUQwu+1X01dYnmzfY+8yKBP/rAq7fPN78/DHz6YJ+36BBF1rIQ8DvfWWM+MW61TrrtrighXXQAUqT4XJ7+zBWNsGIBGADJibDPGbmGMzWOMzSstLbV76UjhxYSwT7UVyjkackRFT/FgWwiN4C3GiLCqMoLjk9mbfC2P2j8hk+q68CbQlT13GpoFelB+mE3xCfm7NKV7qwyvGYRP1JbdFcLHBu2zFXT5shARrnQDdNgphDF2CIAPAdzIOc8YvbMie7aHg+v178xJ/f3W1A049B9jsfuAe2f6ZZvL8e2izRnf+7ELZ2+so5nLT5ZvKcd/F6QnzXX6BhRbaYXKmjr0HDlGt91FAe39eE0ELTcIwhEcchfBflqTkHCVPawr3YdjH/sZ704vlHpdJ22/urYeU9aU4rGxKzXXklWr4AlrYBAR4aoYQDfV564AhGMMM8ZaARgD4H7O+Sx71Qs33zkItex1m/46uejdvqfK2OdKsDVe+Mo03PmZo7QRtlE6e4N9vdmxXPW3h5UKkDAHkbng5Wm4+4vFad85eQ0/LtuGfvf/gBVbGhx+FZOb58avcVNFgiAkEuQ4a7iT5PK6dhdPJCDFF6OWULTrANaX7rN1rY079wMApq4N3grr2fGrcf07czC/sMz2uUF1ebtjjfpwGeOUrH4uIlzNBdCXMdaLMdYIwFUARotcPHn8NwA+4Jx/6bya4eQvny4UPtbvcZmDe9Y7vNy21csH8uW8Iswt3GVyjmfVCYToBJFx1w4mrko4bi/d3JBhPa7CMkFEkTCNrbLHBqPrGd2zl2NTFIMHZAOnPD0JZzw3xZNre9W31Ouz9aUJQW+XBEumcCP/YXruc8U5rwVwO4BxAFYC+IJzvpwx9jBj7CIAYIwdyxgrBnA5gDcYY0qw+SsADAdwA2NsUfLfUe6qHB5yHLxPrwZRbUd9f0ZhQ5AIhH/Rmtq5Uj6rBoi/f7XEdUbyiBGpIDJA5tC2a3819lXVOr9eiBZ1WvT6EiUJJbIdt3NMbV09Ppm9CbUGfl9apZ7budTY58q8LzOW2GF/6sdVDf7NVmWR6TqRxGmzddXcNeeaTVdhWSua9Zm9ldq1RUgqrSJP5CDO+VgAYzXfjVL9PRcJc0HteR8B+MhlHT3D7XqIMSbcEv1efH06pyiVrVp2s/NyohAxC0wnfJ1KAnpBZI6zexGzIDLJ328BcAsAdO/e3X4tTdrBMY/8hI4tG2PufWcZHhOWQdwr/Li9uDj/EvFjw479OP3Zyfji1hMwrFc7PDd+NcoOVOPRXx+he/wnczZh1LfLcaC6YeE0e/3OtDkhDGMG58A9XyzGtIIdOOPwTji2ZzvLc6ymf87DrVCKO2FoV7IQWZ+F+X5Fqvbtoi247oSe3tbDh2iBkcDJg3D98EI4GKoH6IrqOt3vwzyIO6laiG/HKZ4HkQG8DyRTurcKeyprpF+XIAjvqaqtw+hFCb9iDmBreQV6jhyDyatLdI/Xzisz1u0AAHyzMBGc5pWJBfholnE0zbL9ibFCUQoCwJVvhtNNWxEAYzj3EJKQLb+IXk+Wwi3MAlgm4euJsRGu/GLt9r3oOXIMflqxPZSmQOoaedU3vM1zxZKfzY5V/R2pAUCY2ASR+XHZNsPf9LpPPF+nP5DfBiGTZ8etxnszClOfF21K+EZ+NqfI4IwERbsO4I5PFzqOnKkdFmau3wnA3tjw9rQNOF8weevYpVvTEvlaTeuMAXXJyuQIalitjqKem334sX5M7fpGqIUp85iTOofpLmMjXDlpp07OWViUmGDGLd8Wyp0rI6REUfFQO6D1ubJDCGVct0QkiIy7RkWygDmle6swf6NxIBeAnmHcGbd8G3qOHIMd+6p8L3vbHmdl/vPbZRi9eAumF+xwdL62SRftSubo0TR2s7b/yPcrsGLrHuMDVOe/9PNae/VT5ZLMjeHkk42ESfioqK5DnaAvnx7qdZqT1hmeJ6GDTn/zag70IxR7JFm2uTwtxLMebl+KnYHVryFYrQ0xKtOpkBSmASjOZFsQmcg6e3tc7QtfmYpL/51VgVwIDe8lc+Ws3rY32IrAetGlzKcN86q3HcTtfORGLlIWv7kONawPjl6O8cuNd/WJ6OJ2Pus/6kfc8VlmJGpRy4QwrdO2llfYtqhQjo7suiCJUECLKKB9fxe+Mg0AUPjkBZ6VmeNgdPZa02xkFuhmIqmv52nmD542epsBLeKquY9GEBlVm3DRJPQmg2gPq3LYbrFzwDnHzn3Vqs+x3MUlkF39wSyvlczh3uk8xhhQn5x4RNcAWhOw92YUpplcJhag2fSWw0XY1hFjlmzFa9e4u4bbucCtkLZsczkufGUaHrl4IK6VFXzC4EV5Me9RQAsNdh6y+2iB3pZVW1ePk5+aiB+XbRU+Rz3YuzG1U/PO9A1pn73xuUrQUH/jMrSNPuoajjgQtskpLDz14yo88v0KT6793oxC/Eja71gjY6ytq+c454Uppv6P1hURr0dK82xzWLa612kFO1BVW2d6jMzyzI63u3NFMxSh4NdUqe6yQczPG3YkcmzN2mBu2u4UXZ9tKfcpp7fGTrjyvBGpri/qzJp+ungFC0r3obisAvd9s8zyWDPnSLePpKAkkaHcDyEmJVrRYj0WiLSYIITjqWtLMfTRn9LCPstA22z/PXkd3p62QfdYt0xeXerJdYkQYtFFduyrSouyp2ZfVS3WbN+Hv3+1WE5VLMOKN+RXlMnCTbvxbtJMMlGOu+sZJxG2rnkdV4QrwcKsnpngZYjsxUkbGb9iu+73m3YdsC4voEZpWq6ez5V3VXFFbISrIMxh7BRpdey3izaj58gx2K9KvDriRbFoR7YrZVej6FHrnbSqBMc88pNhyHgRwmRfnH00PHvGgFnrd2JpcbnBEQm/kZ90Bnv1O/Qr4t1TP67Cjn3VWFey39V1gmx99aSBiD2ir3jooz/juMd/1v3Nfu5A5Xg57Uvm3Kwo+gCxvtdz5BiM/HqJ7m9KvUTrp1YCKQEtnLgGEIQfmDXN37412/A36XOwj9NUmLpjbIQrs/bAOZfnEKx6eTJ9rl6akIhYtLW8IvMcG9fX87nS1tJp+5MtyDw+diV27a9GUdkBlQmjdSh2LWHqUNnMVW/Owq9enWb4+7kv/oI/fDBP6FphTHOgEJRIo530tBGlSNSKH3acuytr9EOfK/OU36H6nQp1ZgO6k3v4bK556HhR0swCUztXcsYps9taXLQbM9ftlFIOER68nuHSzAJNjltfus/k1+Cx0+NlDnHkc6VBb1z+36LNOPfFX/CzwRapLVQP3J7PlejBYsdZaSn9pq6e44Wf1rhOGtswIZv4XKXtdLgqjnCFdWNzahbIOfc0AXEU282/p6xL+0w7V9mDnXF9+ZZy7FX1HeVUu9Gd7So4tMKUXXPfsDRnkVrX20zh5WZavvi16bj6P+FMpkwYI30DyMX1zNZTZzw3xfmFRfBoTcoM/nbD+OXbsK9KzrojdsKVlp4jx2D+xjIACR+mGet2SMkZwuCssRueY7HzpkUbSYyl/vfOJtVsshy7dCtemrAWT4xdJXw9Pb27o3xl9k8hIsDwpycFXYVQMXVNet4gF6lQiJhSV89xwcvTcMsH81PfOU0kqp53RM409mGyVax5Gaq/N+609htxglF9Pc3zSPvOgRIWwV4m6nbs5PakPZKIPNv5G8twy4fzUxYAlOdKA+fAjHXpi5Bt5ZUAEprea/4zG5e/7j53zJxCexFQhPet9CKg2CnHg/Ff5Jo1dYkGWVnjNpqTfbNAIn4wxrD7gHc7V2Fj5dY9eGvqelvnaJUufpt9ET5g85Uq0fQWJZPdA3r5pxxWRTQ9hoejt7oOn87ZlPJ98gO9+xJ9JmE2cyaC4Y1f1mOnC0W/1XjvtL9LU8ZHrMmX7KmUer3ICleTVpdg8+5M/yQAuOY/6c5660oTTutKY1NCRLph484DUrUB8naXGpi1fmfGd4D9gT41OfswadoPaEEERzif/oczCzMCaxgRlgngvJem4tExK02P0fY/rc8VET+Ud65upu9N34AHRy/XPV5RJDbOz5za/Q4B7bRv2TnNjWms4zxXZCsRO9Rj6y2CfsGOy1I12W8WbhY/z0UPDkLv5luZaQXJKzRrfa5ufHcuzn9pKipr6tIi7OkN6HrClF7UMk9xEb7WzktWC07Vtcr2ZrhxYzubSJxKk51X1NTVh97hVcs/v11uGlhDTZQ2e7R1Jdkqe5i4qgQTVyXmrAe/W5GWgFbN/f9LpO1QR2BVmondnU2/x1Uh00Op5elfzequPcnzSH05NBiFL/cCt36zJXsrce3bs7H7QHXGb1FfFnkT08A/IitcAUB5RQ1Oe2YyBj4wTuh49eQiGrUsKrhtWz1HjsFz41eblyFBY1eypxKle9O3wjlUUbFEIkylRcGhWclLHv5uBc54borBljlT/eUk55vYd1HFi4i2r09Zh+VbypPXp2iB2cIbv6zHTe9Zz1krtu4BAOTpRLGTFYrdOs9V+nEylz3aOu3SWVQaUbTrAHqOHCOxNuJ9Li2KL0lSRBI7CjK9ZvPW1A2YunYHPpcUETO9QHen+ynvhLFPRVq4AoBtNuwktc/fKny7Ffsq7ScgddII7Jyj157nbNiFJ39YZXoMALwysQD7qmpd+U1Z1XXY4xNw7GP6kQ4TdbPpc8Wjr6EJM4ppaZmF/1M2CrlBNLsnf1iFC15O7MrVhXBCIeRi9xVfNLgzAOCqYd1V10hcxK/okvUpKwzrHnLbR/NtCTzfL9ma9vm1iQXC585cLxbO3EoL7kSRlBZcgLpt6Ajqnbjpk6Kh1t1cN8xY9dOK6jp3Pm2Oz0wQeeHKDtqH5cRnQb2IrK6zGY/V9LoJ9BqMrVrqtDdtNu55hbsMhaBBD4zTjdLGOTfN6u1GwEkzCxQIxS6zbMIcJY+LU/8eswEwjq/N03vSmgXKG35sUVVbh+vfmYNV2/YEU4Eswm6va5SbmNKbN85zfS1133W04BLoDD8s26ZTroOyHGBHSFq5dQ9OfWaylLLUC2qau6LDss3leN7CukeEqKSSkbm+1WPhpjKc9fwUFOq47WzaeSDlPyqC3jrj0n/PwJBH9RX5fux0ZZdwpXmetSFyWlDq5jSSoR0TjC3llfhw1kbD30v2Zkr7oxdvMb2ms7D0PONv0blmx74q/LKmNAv3S/xFSUCqr2FTLRIM3px5vjJj/FpzuFncFJTsw78np+eectsep64tNfxNuztY4/HkZ8TionJMWVOKfyb9e4hw0+BzZfO8DLNTsQv4OSbb8rVwUbGv5xfrX9LBxKe37AjzIptI8Jt/TcfLEwtQKzjuTlljPJYrhNGczS+e+GEVCkr24dExKzJ+G/7MJEwr2KFzljnqx6mYSOvxH4HIvG7fTXYJVxnRtow7id5zfei75fh/Xy+VXa00duyryjTLcxjQwoz1pfYiJormFHHiWPjW1A2YtLpU+HwO4Ko3Z+G6d+agnnOK3uQhOckRwlOTooiazLzw0xrp1zSLHqV9NmtL9pn+7jVReldRJZKLL8XnyvZ53prMZ5xrFNDCZToUq2tmowl12BF5I4oyXr1GWam3gLfR8K30+2X7q/HkD6t0BTquKirMw4TX7d3J1b+cp68skUmm7UCM0TZAu4rfd6cXSquLGa7Cy0ZIzlAGqc/nZTpjWj2CguTCkpPPlafkMu/MAlOEeGIIM43zclBVG5BtIOELdrvGfiVKYJpVgLz62CGMEbyCpGRvJTq2aKz7PkQWoJxzeqYBwlVKg7u/WITiXRW2851qsVrrPfL9Cvx34WYM7toapx/eyVVZflO2XzzYjN/4sSyJzc6V0OCk+Wy2cyUbq90Vs/rbecl+Db3lFTW2tYbVmoWg3vnaRq+rGVKR2LkivIKlzAJ1f/W1LjI36KKsAAAgAElEQVRxs+AMajfBqlQOjlHfLrPsM0R8+XTOJs/LcDOXhQnlPtwILNV19Rnzmh7rSvdh2GMT8O70wnSfK8clEzKxM6ZzAP9dsNm1YJUo1/x3xe+ppp77oiThnAuZM4rwz28TufisLYvEe4HRkXb7kR/WTrERroTQtE4znyu/pwfRyIUzCnbohpH30zRufel+DH5oPD6abW8if2mCtRmV4uOjTNBW0SAnrSohbZ6HKBGd9ScfSb1E7/VpvoukeZRDXpmwFpNWl9g+r2RPFT6YuRHXvzPHg1o1kAo642kphDS0vsZ19dhXZS/SrR2BSa3ht4MSVt3OXOZFG7STa3LEi1Mx9NGfLK9ZU5e4wPSCHWmKKjv1v+OzRZizwf2CngiOd6ZtwA9LG4K4WM1rytpG7zjOeaqxylJofDmvGH/7crHr62xSuZHIVLaor6TXTyessj9vGpblstqxEa5EBmQZ0QKDQF3Lm9+fZ5oA2StBQ93Q1iWTyv7zf8syEsyaDRba/FYy2F9dh72V5mHCCeeYRbGMMsrtiPoS+slzP63Bje/OzfheVMCMxqgWThhjIxhjqxljBYyxkTq/D2eMLWCM1TLGLtP8dj1jbG3y3/Uy6uN0gjc77c+fLMCgB8ahpq5easTHN6asczyn7q+qxUezvN91U2O46LM51u2xmZJFzxRM5D1/t3gLbnzXW8UJ4ZwHvl2Gol0HdMduhYe/X4Glm8tTn626i6Lc9CuNQvHuCinXGf7MJCnXEcHJo/FjORMb4UqETJ+r8CxDtHUzEhistAB+LIHVC+3fJ3fRRBqrrN211yal5zYJU9THuCH6aI3ev6z2KD0hb/J6f/5kgdDx//llfWDR+RSolXsLYywXwGsAzgMwAMDVjLEBmsM2AbgBwCeac9sBeADAcQCGAXiAMdbWbZ1kvHPtnDFueUI598TYVRjx4lQU7tiP2rp6y/Zt1QcnrCrBmKVbM+osMjfst7mTJhM3eR2dwFWP2e74mBMzJVdYsNPPjJRc78/ciH9posdal2tecipab735sZvL5AhFoUR12/62fooWCEDU50obWlaM16fY6zBuYWA44sHxqc8HqsUHf9/HXluRDK2PaTBDMz7mg5nGYeQJyaRehLOBxkH8L0fleF3KY2NX4t3pGzypSxqm5sGClyApzCnDABRwztdzzqsBfAbgYvUBnPNCzvkSAFpJ5FwAP3HOd3HOywD8BGCE6xp5+DIXFpUBAHbur8bpz01G3/t+SP99027b16ysrkstPkXGcj/Rzv9zC8vQc+QYbNDJs2N1rht0d65ETybZKtTYtaKxUl5amWArCuuPZ2/CMtWOGBE8sRGuFMx2R7QNWdTP6ckfVrmtVsN1hY6xP5B7KVRlCKUOZ0uh3a2UjXGqcOtzHNWGECH1GiwCWni9gJJ9eSdtePJqOY6+3mN8b3MLd6XMeokMugBQhy4tTn4n7VzG2C2MsXmMsXmlpd61J3XzNmrq6u+LdqVrvldu3YPNkkyE7BL05oyl+72L+nG4G8viuHMVBlNcWfOX3ctYmfup80x+qFEqa8/ctEuCiXsAmhCnqerU54WxW8ROuDITTPyyW/US61sIppWJPFoRv51U3gYyggoFSp/RfxvW78hpayySMVGY4GQsmLFupwc1ScesVlY1FplgLn99Js58boqdKmWWo9QnBuOpBt1YBjLP5Zy/yTkfyjkf2rFjR1uVk4FeIAu9drNjX7p/rPCrZpk37Vc2hg9nmVs0uDFLl9nU9X2utKab27C1PFO4zQnhItINYTTF9ROrdqUOKPWEhZI/Zk3DU4TyqVJAiwRCA6fmYVX4bGttRFVtXdrgOnWt/czUXmJnUlI32vIDNZi9vmFBqr2KxWYIEQKUZmk10MjWHCnRtRrqYV6B8gM1uO6dOSjZax5dUkG9i/3D0q226wf4H+RjcVG6mZbRE4mfzOMbxQC6qT53BbDFh3MNkfEq96hMlQY9ME6sXBcFK+fa2WWRofj85/+Wmf4uqrDzslszpN+r0Rhy64fzccm/ZmR8H8Odq/CZ4logc3itt7ALVNZeRoeFffdGNrJuUe86MkLrq4mNcOUkz5U2J4jasdWv9cn45dtw2P0/Ykt5w6Lw1g/nO75erWDuLntbselPw2xRqV4E3/jeHFz55qzUc82Gzh83GoSrzB4xt7DMs/LMj+F4dtxqFKp8Jb6YV4Rf1pTizSnrBctpKMhJ2PMwkjKpDbgeEWYugL6MsV6MsUYArgIwWvDccQDOYYy1TWrPz0l+FwjqMfsvny40OMYhFuN46ro2xnv14vH5n6xTdqTKilhj59Cvs95tbC3PVBTFcA713BQXEDDHDagd1fPEunP3Af2Euzk5ynF6u53ab5jB9+L49Rh27KuyVdjzP60xTEMgy8rp3emFmuu6IzbClYLZLouV9vvbRZtdlb15d4VtU5mJkuLyK3e9+4CYQ6XsJKraQf/LeUVYkHSIVgYG7bvRe1M5Wp8rIlDMXN8KStz57th5x+pDN++uwKuTCnDjezrhyh2UHaa2FkNTu8jAOa8FcDsSQtFKAF9wzpczxh5mjF0EAIyxYxljxQAuB/AGY2x58txdAB5BQkCbC+Dh5Hcu6+T2CsA6B/3U8SLeSHqwwEqDL4KXgofVzpodgdDdLl3spCvPTXGB4M1xjeDguOKNmTjqYaNcaYlbnF5gbc0ko/0b+2fKm5eKdh3A0Ed/Ft4pqqqtw8sT1uKPHxlvOti9dwrFLhkvI3YXlOzFSU9OxOuCmnOvOLRjc0+ua1c78PevlqT+3r4nYb+vbdCyXkfccjCFiQb/Gmfn3/PlYqzettfyuNGLt6DnyDEothFStrrWWWh0zrkc518VU9eGJ9gFCWjO4ZyP5Zz345z35pw/lvxuFOd8dPLvuZzzrpzz5pzz9pzzgapz3+Gc90n+e9eL+m0SzMuWpjxwUI7XTejXr023JZCIkJ8jZzmjp6C1eh4vT1greG1365C4+VwhhKa4VsjsG5wDS4rNovwlChurSjysRt0cFMV02JZD2udlN1COcvqu/fq7e04Q8gMln6t0zIQAW5pymw+2KLkonLXentO73Y5gVa31pdZhZZ2U64bTn52cKNO/IokQ8T+DHWF1G/x2YeKYVQaCmLo/KpOI0zx1b0/bkJYbTUZfmLV+F35Z417AioJYZBUemJCHdj6TmpwzZVEgWhd7x5lZkSwq2i0skJiX1VCrvNzwzzAc+rt0ouuNsC2cJRAKU1xpAbRsXsZqF9Ps5x+WbUWVSsHoVhHqtB520TZhqyZtVLabADWycq6aISRchSFUpgysE/B698DDPCiKaLrNns2B6lo89N0K62uoHsLm3RXYujvTpjyHFm6hRGb0RqW9OR2w1eFpnTBP4ysma+LQRlgLCuo7hB1kR85SEgL7Peflebit41X02mxPIhxGU1w/sepXZr/f+dkivD1tQ+qzt+l4nGNlsWR1ba8sMb5fYr7J6bbcPKsDVKEyz0ZiG3YuY2w051y9mlZCZf5Nc64SKnMoEs9wfvJc+Z7wSpmmPldelRoCPB50zSaX//yyQcjXS13Fk56cqH8M7W+FitQ7s+g7dt4a58CW8opUm1Kfa9TO1N8rfzs1r/Gqq9Rz4BWX2ng3Y5SXmksiGJy+yyCbwLqk9YRfI/mS4t2YsW4n8nMlmQV6XHFdpZDgC4ubcAUkTHEBjNV8N0r191wkTP70zn0HwDvu62DjWI8TSjstS2kafjWRniPH4KGLBuL6E3uaHsc5cMwjP6G8ogbrHj/fn8pZwBhw+yf6gX5kITIaRS5UZhjoOXIM/vzJAoEjxXrCKU9PxBnPTpY2a34+twiH/t8Y1Nbp+63Y8WdRRyg0q55XgpNMW1xCH0vtko1rLSwqw0lPTsR/F2xOnZsyNTN0qG34+9WJBQCc7xTJWqBorzJh5Xbsr/YnvcO1b8927HPmnvgt8MKKDEFZtrBt9fZbNknobI362RbJyYkvenU6nvxhlaVZoLDpnYtzRfDS95uIFtq2oN0tsdNWlPWVu2iB9k4W9Zvctb86Zcav7V9WfqTWO1tCVUhDZAZz201FhCtfQmWGDRnaiTFLnOXP0aNoVwXW7xDzpxLhQHUd6jlwoKZOt3F+Ob8o80uXiKxpRXdKCH/QCj0HqjOTkALWW+xq1mxPj142fvl2/LzSOmpm4Y79mLV+J9ZuN45+JpbMOv1z6b6qjAUf5xwv2HS4lyHsiDZ7vVx4yrlG5gwU6CJ66CX9tYvRuGunNdhqOZqDRZRqTv0n1eRJCmihh8yeoxtWW7AED2+RCADtmPyJJj2QrSGbaT/KU4J9s1Dfb1pGlM/V280DXn0+V38tGvZNXJGu6nmoTMscBJKwXFz48LKmF+yIxCKnVpXE1U51zR6hiEl8yPuLb4TFz3HZ5j0AGib/G97NDH8OAJ/OcS6M/7xyu9Bxpz07GVe9Ocv0GLNFytbyClTX1mdo1CevLsWJGjPV4rIKvKRj4ldTZ7xLG5bBXqS7yljMRmAYizxOo1rKGrPdYNQX9frJG7+sc1aGqoh8i50rWf3T7WXU87/tENI0Q3pCUEOZtp/e9016Imw7Sn7tvCbTfPFvXy7G+tJMpaaM5N9WGAW5UhPGfiEiXHkeKjOsOQi84NtFWzBmqbwdLYXAmhYTL1soZHpYVqgBovJzPA/AAABXM8YGaA5T/Bw/0Zyr+Dkeh4RJ7wPJSEquUMZQo0R+ZmjfqJcDstGlK2vqcMITE3HvV4uFhHyjOt6vmfzS0U5u9nGjeFGf+6bOYlV96VOe0vd5JOKHVZO6+LXpgtcRa5tWR+kthLS72U7Is/C5chORz21oezVlOv7JWRwtMHKYvSu7Ao3ZXFiyp9JWY/t87iZPU4JU6VhmOOkLblLnSGv/koP46CEiXIUiVKbfeOlILNvm3C1WdTZVcnukIVUGqZve198piTmh83O87p05KN0rJxqek00TtwPdxqRd94SVJUI+V0aasB+XN+Qb0S42pSRxdHNuyi4QeHzsKtNjt5RnRupUqK6tN/TFJKLB61Osd4K80nFYCWEyBQR1SWbRAt2PXfIe1lM/mPdNM+IY0CKbMZsLr3lrtq1WN3bpNlz79pzUZye7OXbHBD92rtSoi7vo1Wmp79ZYmBYGgaVwFadQmX40g+raOqGBvKK6Dss2lzvIcyX3LnwdqgUKS4Vi5+n/Zxm++DnaNcdduElSkM8AXurvP0gI6XurasNteyojgIHLS/e7/wdc8u8Zhr9TnitCBOP8NHrHum9NZtECj33sZ+HrqBelV705E8c9/nPavbgdPupc3CvJVt4QlKuGWblFuw64s2TwYYQOMjiLuuwnbCosxAJauLs5y1DsQDhCZVqxc5/7iHEyxq1Z63cJDeR3frYQ41dsx4VHHiKhVHdYdWBtI5u82tnWs5sdgyzDcz9HIGGOC+BNABg6dKhvw6RZQc+MW21wjpg2XN2Wr35zFj695XgAwL7KhsAAbrS/6utn+Fw5vqpcjPqz+hlYsaS4PO2zYm9/aMcWzitGhAqzPmXW26zMeizNAj2SELy47Kz1CV2wzLV347xMIdDN4E5EF/XOj9ZagHP/hRdTIyWdH0WEvx+WbbM8xm/8UFLEJvbMVhMzFxnI1GxwDiws2g0AqKyRY36zp8I6z5RjNLc+rSAzUpnBoWnYac/zNpZFIvCHR3ju5xgXCjURNNUtZub6nbrf54ZI/att4260ZandXp3f9lTWYPDD4x1f+4znpuCM56Y4Pp8IDsPccR4Pr0aX1/VpcloG1/87zJzS17lfOZkFekNQTUctPD303Yq037jLvRM/FNV+C3++BqXxweeKgHcDt6zG4jRMu4gW0fTWGYRvwu4j/Hj2JuuD4kms/Ryd9CWjc057djL2VNZYHqfGTVwV9eW1ZYn25b9+1pC8UHsNGTlKDujk2ioXSPJNZBe2EqdKnAD1usnCTbulXd81usKfnPuv5xw92zcHAPTp1LALLPp8SbiKF+q3Pn5F+g5PPXcZ4MgXs8D0Mrz21Y2KAgUg4cqQTzQLe6t3+tHMjcLXdtM+ZIRQ1iJyRaNG7UV9FIrKnIUhjjpx8nPUw60TrHbS2K/KBWQ0oexWCRdemSWZoXa4/d+iho1EbW29mjyqKUAFocH5bpGFGbmFv2yUzAIVZPXLgpJ9aWOUsrvwxi/r05RERpBsFTym0QJdBITQ7jRx7od4JI5ebdT3u2LLHvS57wf8vMI8tYofbXjgqB8t6uByU0EAIZ+ruGCn4T/8vWaL1uLkCavMk6DKak9BdDaze9+48wAqaxo05VL7TZhGFp+Jgp+jHdJ8lZzsXJn8ppbvRa4tEordrCJGZZhplc954RfMue9MrNyaHtWonnPkqnqNDLNAPa54fabj62pJ1TZKasSIUV1bjz9/ssDVNbbvqUST/FzD370yu9a2YW23eOlne8m5zbCzUAtDa1XXNz11wnrs2p/pN/72tA1pn2nnyhuCGsrM+mA9h6tGu1OnPVnXx3l5C4sSAa8mrBLLW+kEUZPi/TrWG2nXkVMdU2InXJmNPdZO8cYne9X37L5k2YOAjEa214ajPJGdzFzX4P/kdudKq+GzmyVe1gLFahGp5ao3ZmWY78o1CzTGyURLBMeS4t34yUIDbMVxj09Am2b5kmrUgN2dJ22bft+GlYeda8uaG/Xu7sv5xXIuDuN67tYx3X1Eo+TNIVujWGHVZt0o257+UT84lFeEJRjZii17pFzHjzxXkSIKytSw1dG6gxOEOyprzTVJVphp+NSmqSJt1S/lb0V1utJBzy9SO3lSXyNkordgV/BiHuLgqsAqiT/GLt0ayjw0RnhpNmy2AN1abp3/knau4oWVojFsa8WgEckRe/7LUy2P2bjL2uWEg2PCyu2O/chit3PlFXFt5O/NKExzrNXD+t7tLW6JaBLWyEX1Lk0OnZIR0EJzj8+OTzd/ys9lqKnTCFMyd658uvkgfNYI+TjVitfV16OunmNxcWYQCs4zx4md+6tx47veJ4P3y0Ml0f493CazLJuQjZ22I7OdqY0utu3JjHjt97rTND2DSV2ufXs2piejSHtZ59cmWSdHF0EkF+2ElSV4b0Yh/nZOP9x+Rl/bZURSuHK6iJARiUsGYXJTNMor9MDo5am/w1NbIg546XMl0lqNlifVtfUoKjuA3ia5nNz0hdwca+HKTQlGZ67aJsdMgogmRv3N6Xw4duk2rN3+C84acFDGb8+OV80nqutvFtA4O8N/xYobJQZjxv1URG5y5S9KOGaJjiJBj9Xb7O3QWpn+hmmtaMTYpVsxda1xeh41n8/dZJrsO0woAlhxmbOxKxp3aYNFSac6JyzYZHxu2EOxe4mf+abUg8kbv6z3rVzCOxhDmkQje8LgnGcEinDCqG+X4cznpmDHPnOtlmH9Lfpyvo7DBAfH1yp/Di+62ogXrc0k9NiyuwI9R47BlDX6ScPDP+0TTrEa89eW7NP9fse+6sg3DLdTstmzU5v1OYkUSmaB3pBn4cz27vRCoes4TYljhN95pJzwp4/TA++YzZ//7+uluPuLxV5XKQ29QDEiuF2nxE64euKHVY7P1YZf9wr1IBoWJ8CwEFfzS6IBt3mutIPe/I1l+HpBsfC1jUxrZiWTDu+trDXOc6UqoKrWni12Xm7mRes5MGl1Q6RRN81//kbniiU9lNxDn8/N2nxzscaroVbpn34M5c0bNRjf+DV3MMZMBaglxeWm57tRVtLOlTe0a95I+NggTc+jwM8rzSNny8aqPx3zyE8+1SSdyJkF/uXThSjRsU1VMM1B4KJct43cOClp+HtP+GtI+IGseV22Nk7tnCprMircYT/HmtXzydMxh+Ccp2uzXdzAvV8tcXyuGXsqKBpoHDFray9PWJv2uaauHm9N3WBwdHAM7dkWbyXDlbuNQqpgtTlk1UcfG7vS+Nouyk0cQ9JVduG705UpNVmUL1Hp5k67XOSEq+8WbzH9vaLGXVQyI2QLQZEaIq2iCZL0RdjASV9SD+ra9mY3FLsR6qs89aP+DrhZSVYhYPX6PEf4TYOnFTTY05/9/BQ8e/ngAGtD2MVoh9WsLS/YlO5jsq5U3NwprPOBzHo5FnKY6UdLaOcqu/C7L83esMvwt/dmFOIriSkJwg6FYo8gnHOUJJ3l4mYW6NpeXUotiLChbhdOBq0v5hWl/tZqqNWfZGivGZwJPE7s7TnXPBv7xUqlzkJQNfK1IeTiSzvwqJC6ZB/0w1f30THGu0RaZClIrcwCnSLiT0U+V8Hj5xjtpCw3OdkWFRkH7vjfws2Or5uNRG7nKihkjqV++XbJop6bT0tpWeddlrX7ACU7DSuuQrG7XBSoEyJmOoLbSwXgJjmr7DVVplmg3Ovb5YWf1lgflCTouhLu8Mznysd2oY7kZaVYqRH0kbQaqjbIClqgqa7ICEnCVfD4GuArRINsTg6LRoQNSVBACxu4aacyQ8luKTf2GYsiMvv/BzM32j7n1H4d5VWA8By3E4ZZbiiRS1v15c/nFVk6pcuCc2giKQbL1LX6kQEJf/FjCS3LR8kIv9uy1e28KilHjhsYTELji5xPspUvhEWoCZMsk0uNzxZZtXM1c51YLH49znnhF4k1iRYhGWeIiGA1MbltTxk7V+4ul8E704wd991os/TmpnrO00yDg57UqasHz5M/rMJbU6OfhsL/BKjmWKVY8APtDn6a1QeFYg8NnJsFIfOxHj6WZUW2+fuRz5UNCnfajwDmFlnO9kEShYiGRHjQG5T88iuS0Vb9XL/U1vNQTVpioeyTx9K44AlzC3eh1od5I25KM1mKCS/9oLVXtlvlMI0VcaNDC+Nw7Gmm576GYg9PJ82hxmeLrBKuguDj2eZmbqSIIuLO3MIyXPfOnNRnt+ZIHTWTYJk6SaCLS6dCr5osrmTPdZU1dYH5XG3RMY8kgSl4zh14kC/luH3XYZu6ZLVcp3Py0uJy/Ecg8b3Rcxd5H7Rz5R3qXUXTN+HjELk1RC4kuVkmXLl9zSRceUyxzgImauNjiJQnRATQNpc5mvCubtvTGYenLz7/q4piJKOp+tk/K2rqNAFh/OtsF706zfR3ErSCwa8IskW75PkR6+F76wm4uf7q1WmmOa6AzOBPds0CKc+Vd+SkvYtwjH0FIYrOmm0+V26bQFb5XAXBxJUl6Nqmadp3DA3zQEj6sClWVRQNKBCFeyWMER1aLX2uXNbDbNE/e/1Ol1c3v0/ZTbiypt72AksWO/ZlRuZUl3/X54v8qwyRIsvWMNKQ1XX2V3mTKxNICM5u+niWbR74So7gzlW2Kp2yzywwi6IFLik2jsEfVtaW7MM/v12e9p1a+xSFifSnFduxcaek8LNEZKkUDGVsieRogWqqXWSQVyZNK+2w0+rr7UjUa8IFBq2AUJdfU5edi4ioUOOirccRWbsN//hmqZTreAGZBQYDC1G6jKCgnSt7REq4en+G/TDdYcRtQtUg+HbRFqHjsqz/ZRXVgsKV5U6ny3qYahUl9CfTJiw9z5XGHCUgrej/+2oJXvhpjVg46OQTisrYFTVETb/u+WKxxzVxh9+mVUEErHKD9vkIRQuM1IotutDYlkm2re0afMOd3XikuqrXeTm8xMiOPlu3mInoIbpYsjpsbuEu8wNcsHO/hCTUHk0i2/boOSeHI4nw5/OK8NKEtcEUTqQh2vxGLxZTeAVFZY135nVRhYOn9XH1ukBkfUM+V96RHtHW+F3Qii1ceDVnZlVAi7A4GbrFjzC7BCEbWa121nqXwpXH44C5z5X8srVO7kGycuuegGtAxGX9/PPKkqCrECkoz1V4yExU3/DF8i3+JJgngiWrzAKDXngQ4YTmm3Dh9W6s17oJP7XDnAPd2jZTfeFb0Y6h/uYtUXm8EWiqoeSeL/XNOcVCscuuDWGXa9+eY30QEXmybOcq6BrIJw73tFkVbj4O90MkyNgpFny3YWsDU9aUCh2n1Lu8osb0uBnr3EckTJUJoFXTPNXnkD08wneiYvoVtn4eBdxG06Wdq2Cgpp59/d2tpVy0hKugK0DoMq1gR+rvJcW78fz41QHWhvCKsCz87Q56178jT9MoO4Ie5+KpDMJGlOoqCmNsBGNsNWOsgDE2Uuf3xoyxz5O/z2aM9Ux+n88Ye58xtpQxtpIx9n/O6yD3OCKcaLuPmM+VN3Uh0gnL2NaycXiyJYW17Xm1Lskqs8AoB7QwzsoeLwp3HsDLEwsyvi8o2YvXp6wLoEaELMLS/eyYBe4+ICHAhY/4/Ygnry7BFp1E52aEpR3IhjGWC+A1AOcBGADgasbYAM1hNwMo45z3AfACgKeS318OoDHn/AgAQwDcqghetush+TivCIuyxStWbPHe/1C9YBVJI0E7V94Rxh3jKK95/WLNdm8SLSvPfuGmMke7WJESrmI+lsea0Yu3Bl0Fwiba8WTkf8Xyv4RpPjj/palBV8EUzoNdot7w7lycF/Jn5CPDABRwztdzzqsBfAbgYs0xFwN4P/n3VwDOZIlVGQfQnDGWB6ApgGoAzlbngou8wBeDIernXvD42JXSr2n2yIp2WSs54upzFYYdYzXaUTmoOa0mRMHPwjSv+4Fyv6u27XUUmTVSwlXcNWVxJqZzAqGD1/3UztW3lOuFPw8PHFqzQP/HOCsfMy0xHoe7AChSfS5Ofqd7DOe8FkA5gPZICFr7AWwFsAnAs5zzjLCYjLFbGGPzGGPzSkv1fQGjsnMVd4KWXfVo1ig8ZmKyCMuOsRrtMFyyN5h5RDS3JCGfmesbfKuLdtnPoRct4SrCc7pR3aN8T0S8CWvT9EoAKS6zZx4nC7WwEtZnribGY5beclp7t0bHDANQB6AzgF4A7mGMHZpxIOdvcs6Hcs6HduzYUb8SEfG5im8zAO76fBEWbCyTfl3t2GU3LUvjvEgt2UQJx46xCu1bmVcovy0Q0SHHwZZxpBkDBPcAACAASURBVHrqzn3R8p8gGqDcENlDjBff0tlbWYsalXZyUdHuAGtjjxi+5mIA3VSfuwLQ2oOkjkku6FoD2AXgGgA/cs5rOOclAKYDGOqkEkYJ550e5xVxyTupxzcLN2N/tfwkyOUVtam/OQfaN28kvYwIEoodYzPi29LFibHFgiVOfB0jJVzNKXSZfDRAsj3fNyWUzB68btFxWtP94YN5ePC7FanPUbi3CFTRKXMB9GWM9WKMNQJwFYDRmmNGA7g++fdlACbyhJSxCcAZLEFzAMcDWOWkEsLzOEsIODUCgRC8YM6GaM3HPdo3sz7IY3bsq0r73Lppvq3zY9r3QrFjrDkeZfur0z4T2UuuV8JV2JwNowiZBRJRI6wTSjZr0MJAUIt5r0lqxG8HMA7ASgBfcM6XM8YeZoxdlDzsbQDtGWMFAO4GoMyHrwFoAWAZEkLau5zzJU7qYWcaf3ViAfre94OTYlyzuDha1gidWzcNugquqZWcCiIkhGLHWE1VbT2ueGNm6nMsnzohjBMTbEvvSJWz4dlINPC5jLHRnPMVqsNSzoaMsauQcDa8EipnQ8ZYMwArGGOfcs4L7VeVIPQJod9xVuO1UBZSmS9r+HjWRgDhFb7dwDkfC2Cs5rtRqr8rkZjXtOft0/veCXYm8s/nFVkfRAAIn1JmkwMn+Xemb8A/zj8cebmRMjqyIrVjDGAzEjvG12iOUXaMZ0K1Y8wYU3aMPwLQDIkd4xfdVuim9+ZibYk3Ib6jSgyHe2G8MgsMnbNhFAnbwB4n4vpko7pj7PX7GLuUwvoHiRe+KEQDdnypgg5qESXisjjcbTO6Z9gJy46xmiUR25UlvMVJCgSRuJ56zobHGR3DOa9ljKmdDS9GwtmwGYC7jJwNAdwCAN27d7d5C9EmJuM9IZkw7BiHtW1GzRwpbsRlkRpabEzklFQ2+9h9oAYdWjQOuhpSCcOOsXn9vC6BCDNOXr/IzlXonA0JQk1MlxeR3TE+6qHxfhVFBAKtNLxEdDyrrq0n4coGcWm1HVvGS7AiiLBT5yCZs4hwFTpnQ4JQE5dJU0Pow9MaEaKk8oQHkBbXW5gNgYlEKxvEpN22bBy/RMJE+CnZW2V9UEypdzDpiQhXoQhPG3kM3s228mAyfxOhJ/AdY1pEE3pQs/AWOwITbVxlH/TOCcJf7Cb7BgSEqzA6G8aJFVuzMr6HVGI619COMRFK4hglMEzYWTyTWaA4FFSKIAgnNMnLtX2O0P5y2J0No8DsiCVcJAIn8PC0tBgh9CCzT2+xIy+RbCVOXHQCdsxGCYJwT9+DWtg+J1bJEsLM5t0VQVeBiBC0Y0yElZisUUOLnVDstHMlDrVbgiCc4EQxQ56RBBFSaMeYCCNkFugtJC8RhP/I7HeHH9wSq7btlXdBInLQzhVBELrQGpowg9pH8NA7EIeUAgRBOMGrPFcEQRAEQfiAHZ+ag1s38bAm8WLBpt1BV4HIEsgvjiDhiiAIghDGSc4PQhw7y7LCnfs9qwdBEAThbNc7MsIVbekTBEEEDw3F3mJH6b1x5wHvKkIQhCNo34qIjHBF4X8JgiCCh3auvMVOtECCIOQg05KPrALjRax9rmjniiD8hbocYQblQfOGRnmRmZYJgiAIHSIzitM0ThhBzqMEQcSFzm3iF6TilL4dgq4CQfgGLUlihgMBJDLCFZmiEARBBA8Nxd4SR7NAUoAR2UQc+zBhj8gIVzShE4S/kNkXoYfSKmhM9oY4yiE79lYFXQWCIAhHOFkLkXBFEARBCDNnw66gq0BEjBVb9wRdBYLwjTgqSAh7REe4Ii06QRAEEXNoYUYQBBEenGzuREa4WlpcHnQVCCKroN1igvAf8tcgiGhDPZiIjHD11I+rgq4CQRAEQRAEETOkKjVo+zlU5Oa4ex+x3rnKViX6ib3bB10FIkvJ1j5HiEHtwxtoXUYQ0Ya6cLho17yR72VGRrgiCCNoICMIIi7QeEYQBCEPty4OTk4n4YqIPKRBJwgiLtDOFUEQRLQh4YogCF04RbQgCCIg+nZqEXQVCMIRpCCJF07WQiRcmdClTdOgq0AIQOOY/1DfIAivoBENIIsEIrrU1VPrDRMXDe7s6vyaOhKupHL1sG5BV4EQgIYxf7liaFec0rdD0NUgAoZ2Nr2BtN4EEW1qHSzGCe/o1bG5q/Pv+Gyh7XNIuDKBhWCWo/ULERRGTa9Ns0a0ACQIgiAIHYLeuerUsnGg5YcOlwtpJ++ThCsi8tA6318SOxb01LOdHJKwPYGeagJ1apoLjzwkuIoQWYHM4axlkzx5F3MAjc3pBCHqknAVAygXFuEntJtKAGS+5hVhsJhwwse/P07q9dQLxKcuPRIP/GqA7WtQUAwiCNo0yw+0/IgOIbGChCuCIHQxEqI4aPAmSDvqFVF9qkd3b+PZtZs3zsN5g+zvXuXmRPVpElGnST4tr7MZevse0/+QVkFXgSCkwnl0F4AEQXiD18K2k8vn58ZjidM4Lx73EWbktt5gZ0hSfKUThLUN9VgTotI+/3F+/6CrQMQRw50rsgskomu+Fnai+ljzJO8SaduXk6vnxGTnavWj5wVdBYIwpFmj3KCrEDpIuIoBg7q0RuGTF+DyIV2DrgqRBXAe3QUgIQ9qAt7AIvpkZZvgybhabjQfJREDguzHfpvDylasxAESrkyI2iRXUVMXdBWILCFqfYOQDwnYhBrZO5kZl3Nw+bwcWuIQ4efIrq2lXq9zmyZSrxd1gsjJSCNPjPCi/ZBCInsxMv/jnNPCmiC7fo+gxyoPkq2IoLDTj2Wv3fxWfobdRJxCsRMZ2PFveeCiAbjppF5Syw9bp2kVcP4IIpiBiggfIRsapMAYG8EYW80YK2CMjdT5vTFj7PPk77MZYz1Vvx3JGJvJGFvOGFvKGCP1sUScLBijEi1wsOSdiygR1z4XZMuL49gcNUi4MiFqDbRTyyYY5SAXiBlhewTH9myHZy47MuhqZDUULVAOURtftES8+hkwxnIBvAbgPAADAFzNGNMOqDcDKOOc9wHwAoCnkufmAfgIwB855wMBnAagxlk9HFU/9jh5LrS7Gm7C0ue8wI4SUnaQKL+bfRBmd2FHSLiKq2aBsMarTvrIrwc5Om9Ql9aZUaRo/vQE4zxXFC8wzPilrQ/brrYEhgEo4Jyv55xXA/gMwMWaYy4G8H7y768AnMkSD+IcAEs454sBgHO+k3NOTrAukCEYRWXnKosJXZ/r0qap20vYJupmgWEnlKHY46xZsIKap3ed1OlVs0UTGWaFBufBDFZxw6uWfFKfDh5dOZ0YdsUuAIpUn4uT3+kewzmvBVAOoD2AfgA4Y2wcY2wBY+xevQIYY7cwxuYxxuaVlpbqVsKp0HpEl3iZlmkfg5OnkhuyRtooJnm3JOJ5nxNF6Xeymoydy9B8muB3x3cPugrSEOnpodMsED4SrrkJjIWuStIJi0LDaLyP+zxwVv9OQVfBFX71jxgqOvRuSNvcjY7JA3AygN8m//8NY+zMjAM5f5NzPpRzPrRjx47ClchGZOyMhm3nqv8hLYOuQtjwvM8BYkqNhmMt6yxEkDv7fhct615vOFFuzIAgERGuQqHNCxvDerYLugq+4FUfddoXc1gsNeZaQq3Q4DzeiYTbNW8UdBUiQQy7YTGAbqrPXQFsMTomqchoDWBX8vspnPMdnPMDAMYCOMbzGseYfI1g5GQBd9PJ3izWHrpooKPzLj5Ku3TKenzpcyJKDQWvlEYDDmnlyXUJa8IaLTAU2rwgMOtjfi3Agt4u9lKQefpS+4EpYujnoYcvphLOlRo88HYpk8MPTtcm+2Wv7kVbPrRDc9cTycGtxKxIY9gV5wLoyxjrxRhrBOAqAKM1x4wGcH3y78sATOQJb+5xAI5kjDVLLgBPBbDCSSWcPte4KTzc7joVPnkBjj+0vaTapNOpZWNH5zEGtG6aL7k2kSYUfU6NV8PaZ7ceb/hbveQJ1c3c0qKxnIjMx/XKjg0II0SEK9LmZTFeLjQ7Opig6urjtYAwwBdTCSulhlEEoMTOVXw4WeOj5JfQ4EUxFx/VxXXkpqaNcoWOi5uiI6mkuB2JRdtKAF9wzpczxh5mjF2UPOxtAO0ZYwUA7gYwMnluGYDnkVgsLgKwgHM+xkk9yBk9QX4E/JN+e5w9H5E4KaVkEJY+p0bGzpWe+0LY/P+MsBL0jj9UTGhydrvmZZ92WHg2X6wQEVFTmgUAm5HQLFyjOUbRLMyESrPAGBsH4F7GWDMA1UhoFl6QVXmvMZvkZPSTm07qhXemb3B/IQ/Jy2WehCBhcOY8tWNfFbq29T+aj8/YUWgUGyk0AIAxpig0JsiqXNwWCBmO8z7NgYlJXPxhvv67IfjjR/O9q1AS0fuPxlLBHpzzsUgoAdXfjVL9XQngcoNzP0LC39EVjneuYtYvtYQp3DMHsOGJ88EYw8ezN0m5Zg4DskN3mE4Y+lwaNvtf17ZNUVxWkfZdqyaZu5Oy5pWDWzXBtj2Vhr9fcnQX7Nhf7fj6MrrZkB5t3V9Eh/zcHDRvlIv91fY8HYIYOyxVQ2HRLFxwxCFOTvMMGR3l/gv6u7+Ix9w74vDAytaLflXPeRzNkbSEzlRCzSXHdInVQi5zBya4BvbWdUMNf2ve2HpHSUbfEL1EDANaECFiYGcxH5WgmqHMnVsO6k9hod6mhPuP8/ujc+t0U+o7z+ybMZCaKetF59NLjumCww2Corx57RAUPnkBnr/yKE9nMJGddee5SPWvfckx/vsqvnTVUa7OF9p355yP5Zz345z35pw/lvxuFOd8dPLvSs755ZzzPpzzYZzz9apzP+KcD+ScD+KcOw6VefPJvfDRzccJHfvejcc6LcYUtQ24DNMNP8dSp7kbrhzazfogB4hYfDx0cabTcF19/M1mwqLQMBrvjzu0vcmv3nBib298JwD7O1dXD/MuXOxZAw4y/C1IX7DbTuud8V1ebrz7YVA4faoi80mUzGoa54mZBXIOdGvnrzWDF8olEq7CgXoX6tJjuloenwiylf7uurdvlnGc2esV9Zcc0qOtoWvEOQMPNi3rvEEHZ37poi5mVNXWOzxTv2x131COaCQ4Pljx4pX6QpTb4DPhN2pOwhhDq6ZijnY92jeXVGb6Z9lRXcPms9A0P1Mz7lUVGbNeKupNNna1SlElDAqNMOHlTlnhjv1pn63a5c06EcgedZIUO1zdP4VetYb3zVyUU84ej3DYLl67xtqdOQyRMEUVfZ1tKASn3nuG0+oY0raZceAJT4KHhHQ8yBaUx1+nmmxOP1yeMsJUuNJpTmdrFG1f33YCrhnWHY9cPAin9jOv1+TV6QGqenVojn/9Vizcgd0l1t1n98v4zmnAFyP01t7aaKJG/PL3001///XRxkLULcMPFSpDj0jNjqILrHyPNKrN1VFU/HJ6l5ZzwfoYvQnDq9sUce7U6zt12WEWGArM+pvfZoFeRkJbsXVP2mer9tW7Y/O0Y9o2y0d7HxatIu2ewf270YvSprdLFYWAA1HETO1kFM65TbN8IaWijH771KVHuDq/TbN8oTnariAoe0HnFc2TAWOu0QTDCFlKrqzj3hGHoVFeDrq1Td91OqZ7G9PzjPpUrw7p/dHULFDnu+EaAapXhxZgjKFnh+Z4/XdDTOukhUFMmX/+EQfbNkrRRtt978Zj0b6F076oX0e9OUl0p7d7+2aW41675o103XTOHSi226dHLGfHVh6FOlUHUhD1AXPrSPeHU5xLzmp+LbDFqaexcLu7ZnR6jkDL03t0+jtXNCv5jd/ClZcbltpBWvtZMT9467qh+OZPJ4Ixhj+f1if1O2NMSPDp06mFq3r61crP1xnb9CYyMguMHjJCPtvti1oz/UQkNeu2o21fVsXOue8sqeGfGWP4+7mH6f5m9Bgf1jFl1/LxH47HvSMOw1/O6JP2fdzN3cPOiEGHYM2j56WFIj97wEFooROcQk091x8L37khs90bobdOPKl3e1x7fI/UZzepCQbp+K/rcfjBrSwVmVcem+4qIjr/iaFftt46NEeiNmLBP8/G73XX2s7Hy0gJVyK3ecXQrrqRWmRjtS2r4FY4ObO/sQ+GHe4+ux/m3X+W+UF6whWAT/9gnJ/BCqO7z2EMzRqZm3nqve863VktO0wFw4TvOXUMivvbOZkmCXbR7qJmtNlk2a2b5ePo7okoSOpFKtM/K428HIYxd5xsXo4kRN7NTSdlmjYq6Nmy5+lMZG7zEBH6uJkypo80N48LwqpaV4MvcI+DOostCL2kd0d7CpHrTuhp+vuAzq3Qq0Nz/Om0PhnCFHWncKDeVW2cl2upIOfgGKkT+KuDZvfG7PVq04EAibXjX85sEMDTfP4t2srvjk/fFX3aRoAJK/2L1oxOay3hxRCjmABWq3y5wt5fIiVciaDd0hVBxFYdAC4a3Dn1t/4iP7zk5LCMzq5F1yyQAYd2dO7DZjSZ5zCGQ1qLJSsVuR4hn8b5zocHrcmLlj/pBEgww0jjfmzPdvjH+eYRLfta7BhlBrSwHrW1/V8raGij/p3Quz0a54nljzJEU63HfqPv5yUyNI361QDD3/TO1xOkIjYERgaz1tezg/n81qVNU/x896mGv8vYuVJfQqsw0ENvR0Y9lxrRSZPM2qjq953vTdRdBqCqVj/ks52n+MWtJ6T+Vic1djLuED5g8z3k5eTgPNVuv9E6y+z9dmrVBNef0CPje3Wb11NwGdGmabpJbZOkP72VD5GUFujBvHBU0jRT7YdmJwCMU2Wwm+EyUsKViImdk/GpbXP9nS5tZ1Cb6Ik29CgNl3qPVyTwhBNyc5ihz4aew7MiiJFZoH+Y7QBbdcXHf2Pul2E3xL9RcTk5DLcMNxfUHr/kCFw5tJvhbrPZDkyT/Bz0Tgpn6ntW9wrGMqNfnnpYx7SAD/p9y7TaabRtlp+xSHWraGjVRCxAEJCYyOardr6vHtZdOFQ2IY/7LxiA+y/oj9Ympu9m5qey870MFNhd0rbzreUVeOISd35bCqMuHIA/qBaMstd1NXXur3hQK4PFturvE3q3D70mPlsY0j09R5NVl1EW/Mr7++CmYbrHWb1eXUsd1SBvR5gwMpn7h4UiIjGX2WuIjKX3cUWBI7pyfPv6oeitUuCrlfnKsx3YuTXWPnYefnd8j9T70K7Pbzypp2EZyjl/OMXYYgMAzurfSajOIkRKuPIKvUZww4k9M49TvcxmjfIw4Z5TccXQhlCdek6G2obqR26rwicvcHSe0RjSUcdRWDRS2F1n6Ztt5TBjnw09R+Z/XpjQtNfVcx0NEKnQ/UbGEz/LhsmrelHYUmUTLzLhMABPXXYk3ldNehueOD/1t9YMTn3JRaPOSTmgq/nz6ekCnbYeDEB/lfChHyxGfBJbOOqcjO/0FsqMiWvb7LzDvFyW5qT8xCVH4NZT7e0+EmKYabg7t2lq4BsgRr1AhOSOLRubBpNQ2rKVo78RVTX1joKhaB/LL38/HTfpRO6USbVBSGk7QqphP1d9fe+5hyOPAsSEgjvP6pv22WrXQ1njKUe1b6Hfd6ymKr1dZbVwZabQ//yWdNcNkYBhRpiV09MgxLy6PK3Sz2oj4sz+B6U9YXXKpZtO6oVVj4xAv4NapsYMxWqkkWb9aGdnT0HrU6ldw7tZ50SqN1vdaKeWjfEbgbwEWrTt8Pu/nIz7LuhvOYD27tgizdRHr1Nphatjexo73J47UI5/lVOM7teJucJTlx6BifecijvO7KP7ew5jhp1B+VpdH2XxWsc5erSzb/pJyEV5NZeYhDG14s1rh1iGSU2VZ/C91p9DFHWb7tw6sVOqOMOLCD0tNbt6uto+VfvtY9N3QwS3mxDaXeAOyfFLbzFBOXj8Q+RJG5mrWSFiFjj7/87E3PuM/XOVSxxuELlQi7bpWNXh69tO0PUdU592zXHd0wJMeUGP9s1QUyeer+cRm+kYlHGmbbN85OYYz4eEv2h3hfWa6893Dzc832ioNFtHHdernW45rVXpANS7Udpjjzs0PQ+k02BDjLHUXHbnmX1xjiYc/Am9E75hagUnA9PULb1yH9w0DKMuNDZD19KiiVp52mDSqKDMW/kapahd/0gg813JVHBESrhSMGqjc+47y1GyXOVyPds3w8qHR2BQl9bIz83RnQQ+uvk4vHuDfpLiJjo+FdoBc3C3Nphz35lYNOpsAOna+yB8GNQ7UIq28l0HSZi7awSeK4/tjkM7tjAcUHIYM2zISkdVP/+UdohzDO7WJm1w6y84yRPyKCo7AMBNssDEe9ZLtggA/7luKD75Q4MGS88EbvED5wiFa7aSC/5yRl+8cvXRKRMEu0mFAZa5c8VYSkS5+eReuPvszKhjVkkQtXl2RBapev3tqz+ekPZZ2dnWPtOLBicE5Z37qjOuQQs//xCRYytr0vvd7aenK7F6G/jJipiS5uSwNGXBy1cfjbvO6oc7z+yL9s0bCWlz1fOKtk1qP3fQKCWH9GinO483U4Uwf/w3R0iNFqbHU5cemRJitbnt8nTC3V5moNjNNVjoKo9BGTsUzbzbUPeEe7q3a2aaLL5Pp5aGv9l1pLhsSFcM7dlOt185Dc5mRxl26TFd8SuVD6SyLjur/0EZYdaVy8657ywM7tawc63uirqOGwbVUXbc1D+bjR0AUJscxLRzkjqKodZ6S6mT+noDDmmFcwaYh1rPGp8rBTdbnkBCiFBHZ1FeVk4OQ1OVCdApfTtmZLQ/uW8HnH54pl3mrcMPRf9DWmJIj7Z44crBOLZn29Q1tXRq2QRtmiUmFCWKS5P8HN+DNTxxyREYe+cpeOKSI/DilUfhv7edhKcvPRKnH5Z5fydoNCNq7jyzL36593QcnTQTEdmBM9PUKe9X/TyU/qZsk6sHt78amB4S8lDCKR+a3ClSzGUOOzhzklGHjxXhx7+ekvFdu+b5OK6Xqs2lOzwZ4iR6YH4ew68Gd26w5UaDWbCI/Tlj+pOZcr2LBndO00AqNEkGDDGK3Ldw1Dk4tmdb3JEM2awtwWi80O48De3ZLs3UouH89OM6JX1D9lfVZhzbTMc0kggO9aLn+7+cnGEqOOGe0/D0pXoRwuxPMhcN7ow7z+qLu87uh/n/PDv1vVnPUM+j2uO0n7++7UShejRvnIe5952Fhy8yCXcucHtvXDsEd57Z1/K4vge1TOW2VOcXu/SYrhjeLz26W68OzdPuGQDG/XU4Hvn1IHRO+gtrA+u0TGroFZ8xZazp3bEFjj9UXkh5wj6/3Ht6yi9Q2Mw6eZzZlHFK38yogIqSzaicv55l3Va1HHaw+C5O66b5acqM5o0T7Zgx4I4z++omCW7aKLfBXF5jFqjsXIkExFL8Q9W3np8mXBmfqz5ucLc2YIzhpD4Na4ZT+3U0tawZe+cpwulRjPILmhEp4UppfH06tUC3duY7VGp78CM0Mf5Hjjgcr1x9dOrz3soaAEBTzfZj/0Na4b0bE34aR3Uzty/v0rYp8nJz8PVtJ+I3RzdosKwEwcbJjpXDmPCLPriV/Sh7CuoIfd3aNkOfTi1w9bDu+PXRXdC9fTNcoclhoPDM5ZqJWnVbSuhS5V5vPtnaJ6Cunqc6R59OLbDkwXNS2teUCaCOM6eefzGFhPae0w7rhHdvPBaf3ZrQNNUmnTe0i+5rj+9h2zzm8IPTB65+B7VAv4Napk1SIglSAeCW4b0td4QUtF1TMUdo27wRRl04ACsfHoH83Bw88KuBGNqjLY7squ+8/6fTeme0QYYGIcdoCFBMis3Gsi//eCLuPkc/185R3fTr87vje2T4iZ2sM6lrJ3PFHIbzhEmUGmXh+NUfT7D9fgl7iGi+X1bNX3UGUvYVx3bD+LuG46z+nfD674agSX4Ojuhiz09Kt21qGs5hB2UqWNSmQUq/SjmLa3eGVV9ozRHH3HFy2g52x5aNTU13RPInnjvwYNx1dj9T0y6Fq47tjrevH4pLjumSmjsf+82gNLPgn+8+Fd/eflLGuYcd3BLXHt8DjDGsfew8/PjX9PIa5+Wi8MkL8Mek76KibLTjN0l4j90Im2bmf//RRJFNJ70cZT3617P62fajP+Pwg3CPjlCkB2PA9j2Vqb//ecEAnNSnPXq0b4a83BwcoZr3tDvkQKI765ksPnXpkbh6WDcc06NtxjkAMP/+s3STDavnUrOlnVFuMSXw0vs3DcPzVx5lfAEBlHFMETjtECnhamDnVhjWsx2euWwwpt57RloGa+1L+ESVm+m7v2jCxbLEAkpBMW061MBmc9r/Oz1tgFdzy/BDMeCQVrjwSP3Qsla2r0rHzWEM95zTD5/8/jjcYaFVa6OjBRdFHRa2RsS7OUmGAKMaBzontR7K4kxE1plTuAu5OQwf3Xwcvrj1BLRqko+/nXsYCp+8IDVB1usIV/rRAgk/OP2wTujUMrHAqE1KuVrH9ONNdjhF+OT3x2H8XaeiZZP8tElKN8qRQQS+8X+1XjQBSEW7U9rWRYM7Y+R5h+N3x/VI28Ue1KU1vrrtxAzbb0WwvGJotwzTJkCtyWy4D7UCRzGlVSetNEM7aQ/p0Q6vXnN0xnEXHtkZyx46F38/97A0ZYpWYFLGntd/NwTj7xqetqCbovGFU+o9tGc72zuThE0046daG6vQTyXQmKUF6XdQS7x1/bEYMehgrHrkvJQlhtbMTY+3rhuKr/6YuaukKAUU08Tv7zgZqx4ZkXaMukatmuRj1v+diceSEUSV27tsSEIJqTTrTi0bZwRPGti5NU7snakYMOKOM+xr+c3IzWE4s/9BYIzhhSuPwsjzDs8YB/p0amFpvpWfm2OpBFQWoc0a5aGDThApIhhEVxxKlDuz19wkP1dXEQc0zBf3X9AfH948DAcLpqpRbxSoaWkjGqwiRPTp2ALnDDwYH//++JQCQWnb1x7fI7XWS6s/SzcjVpZoB7VqgicuOTKjvyioBSujR9ano7H55T/Oa1gTKJZVjht/1QAAChFJREFUjfNydQU2tVXKRYM7C6dfaqiffQW++NMPAU3yc/GFyn/g/RuPxS9rd2DSqpKM7VajFwoAbZMmebk5DHX1PBVuNd9AEOpqkjurW7tmGHtnplmTgrKwunpYN/z93Mzw00rHbdE4D/m5OTixTwec2KcDXp6wNnVMv4NaoGhXBSpqEvbfL199NGYU7MCD360wLNesvmce3gkTVpWgzmGY2b+fexhembgWqAPuHXEYfpPcen3m8sH4dM4mDNFoKp67fDDmbSzD1/OLUZ10EL4umdNBT6P+lzP6Ys6G2WlhfhWtntrk6fXfHZN6l4S/KI7eis/TdSf0wN1n90uZu5rxwpWDM75r2TgP5w46GCfqJFMEkDbRmA1zDEDPDs0x+W+n4bRnJ5vW4/krjsLk1SUpR9jmjfNSWmQRlHrUc45DO7bA6NtPwkWvTk/8ZqB9/unu4fjNv2agdG8V/nRab9x/YX/069QSj41dqWuOl1aezo031whmynjDGMOfT++DP6s0jeP+Ojxtl0NZlJ894CDk5jDM31imW86c+86kSGYBcd6gg3Hbab1T7UoPo50rPQZ3a4NVj4xAk/xcvD1tQ+r7qfeejlOenpR27FkD9M27lUASrZom2l5+bg4ypltNlQ5u3QRl+9N9+Z657Eg8femR2Ly7AoAc09MT+3QQ1vKLjFVqjj+0vWvlkRkPXjQQVw/rhsMPbokHfzUQY5Zs9awsQpw7zuiLG9+bg//9+SSs3rY3lceqW7um2FfZMGa/dOXRmLBqu2maBD2UrqIou1o0zsMpffXThjSckzi2SX5Omr+UmmYGSruJ95yKVycVYF3pfhzaoTluP70P6jnHkJ7t0vJIKQzp0RZvXjskbTMDSJ8nFKul7u2a4fTDzev+zZ9OzPDrVeYxtQKxU8vGuub0Cif0bo/CJy/A7PU7MdQkUBzQsLbPz81J2/XXQ20G6UaVHynhSgtjDKf262iYv0aPfge1wKAuCY31zJFnYHdFTWpRkS9iU2ATRaJv1TRf1/m+VZN8/N95h+OcgcaOdeOSmvhe/zcWANCzfXP0O6glenRoji3JicmIds0bYZdmUrt6WHfM31SGvgeJ2+VWVCcEux7tm+HPp/fBxFUlmL+xDBcccUiqQ7Rr3ihtMadw6ZCuuHRIV9x/QX8MfGAcgIRWw4iT+nTA+icaJshfH9UZw3q1w2+P647bVMlnRww6RO90wgcUP8UBnVth0t9OQ9e2TTN2sYb1aoc5G3alLdwWP3CO7uSz9KFzhcvu3akFFm7aresorrTFnh2a47vbT8aD3y03zMXT76CWaTsAdsnPywGq61ID8JFd26BNs3zsPlADztVOtA3ndG3bDBPuORX/W7gZJ/Run6rvt38+KfVMjVDvQj17eaaA2qllY90UEgpahdPDFw/CE2NXpjStvz6qC+YVluFvyfC07980DO2aNUrtVhL+oJiKjxh4MP6tk95Dy9EWJuta9BSPnds0RW4OE9qVPKF3e7x89dE42ySVAkdCiaINdKSGMQbGEqZ+x/Zsmza2y+TNa4egrp7jto8XpH3foUVjrHx4BJrk54Axhp4jxwAAxt81HDv2VnlSFzNaNM7DkB6JRWLHlo3xm6O74JuFm32vB5HOyX07YO1jidQd6rlEG+n2iK6t00zogMRa69M5m/D1bQ2bAvm5TFchMrxfR3wxrxiDuljnjlMw21FpYyDkHdqxBZ6/ItNczqzvm61PGRrmuLevH4pmjTLFCnWMg6O7Z5oJvv67Ifhm4eaUT/f3fznZdI2I/9/evfxGWUZxHP+9My0zvVBaSgcKxQ4KWOQi0SqtDUGhUREv0RhdeIkuSBMwQTZGjTEmRlyZ+Ce4MqbGCySGhHhZGFxAa6iKeIHQUii29DJt6QxlSl8X4wzzdu4zT21Lvp9lO528aXLed86Z55yjm8/6mZMSk3mxqV4D45NZ3WPiT45tXV2p+/xVeveJ7KcdRi3o5CqTu1dXqqs3ICnSNH6kq0/HDu6I/d5X4ZWvwqvzgxOSpPpl5kZ8N99erZPdI3q9dZ0sORcQz5RpX0x8Nv9qiz/WU5Js8ETUqsoSXQqE1N7WnFARb71ruU4l2ZsTz7fYo4G4B0x1WaRas//BSPL08fNb9fOFkbQPz5nKPEU6emC7fvhzIOs9J+cOPSaXFfkffJBhMS3Me+PRO7U8yYfrbWuWqmcoqHJPUcqb4Gf/Hc11uSy9s2eDahZ7cq7qSTc33r//1Ebd4SvXhhUV6roYcByn27OlVt/8ctlxJGNz3ZKEZvn7/Ut1ons452tIpr2tWUdO9TnG0n69r0XHzw3K5bK0s6FGZy6PqWbGMYUKb7FebvY7fhbfU7Z3+xpH7EXVVZXqxNu7tKzcEzvjHr+W4NO9TQlN9em81FTveKCWLHLro+duJm25FK1gjrfYraMHtjtGjT97b52+PdPveF1jfZU6ekby/lbxlQf8+uSnbr21u0Ful6Xf3nskNmQlHcuy9GSKanm0kLKzwefoPZYivVclxe7YzsIob7Fbnyc5fmjKwxtXxHpKZi70TRYvhRZd2tuaE/q383Gwdb2Onf5Hh1+LtDUc3t+iC8PBgt8XZmSzoubDZzbr0NObHK/9al+Lvui8qLW+cr355a+x4sjjW1Zqx/qahDUfyUSnVe5Ks/R2Vw57JPNx29JSHdeQFhW5tNZXrnNXJlKeGFuXIZ5WVpY4CvPpEkx/dam6h3KLA2+xO+MC5VR/l++9yTK9sb1QjY2NdkdHh5H3Cl6f0mgorNol6Ydf2Lat784M6KEGn7HhCDembV0aCaUcNZ1JXyCkq5NTed/kT/eN6lRvQC9sy68/4q/+cfUOB2c9QGebZVmdtm2n6yKFco+78Wth/d43llXVqBDf/9GvDbUVGWP4+tS0RkPhpAuv410L39BYKCxfAUNhsnVj2tbQxOSsf/MzdHVS3mJ3whHBuUTcZVbos256OnI4aL4N9Lk8GlJ1mSfrwTL/l+NnB7WqskT+FLvx/u4fVyAUTruLcj4j5rJj8jNmoXqHg6qrKslrl2hfIKTq8kWOXaszDYxfUyAYLqhYkMrIxHX9eHZQuzet0OTUtE6eH046STuqs2dEZR53wgCrXA1dnVTPcFD3JPkGbC6kirtbOrkCeOBkh7iDScRdZsQcTCLmskPcwaRUcTe/SksAAAAAsECRXAEAAACAASRXAAAAAGAAyRUAAAAAGEByBQAAAAAGkFwBAAAAgAEkVwAAAABgAMkVAAAAABhAcgUAAAAABli2bc/1NThYlnVFUs9cXwduGfW2bdfM9UXMd8QdDCPuMiDmYBgxlwXiDoYljbt5l1wBAAAAwELEsUAAAAAAMIDkCgAAAAAMILkCAAAAAANIrgAAAADAAJIrAAAAADCA5AoAAAAADCC5AgAAAAADSK4AAAAAwACSKwAAAAAw4F8ueNSYIfEUUwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x648 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dismod_mr.plot.plot_trace(dm1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dismod_mr.plot.plot_acorr(dm1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running multiple chains\n",
    "\n",
    "It is simple to run multiple chains sequentially in DisMod-MR, although I worry that this gives a false sense of security about the convergence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using stored FE for beta_p_x_sex x_sex {'dist': 'Constant', 'mu': 0}\n",
      "finding initial values\n",
      ". . . \n",
      "finding MAP estimate\n",
      "\n",
      "finding step covariances estimate\n",
      "\n",
      "resetting initial values (1)\n",
      ". . . \n",
      "resetting initial values (2)\n",
      "\n",
      "mare: 0.04\n",
      "sampling from posterior\n",
      "\n",
      "CPU times: user 19.8 s, sys: 4 ms, total: 19.9 s\n",
      "Wall time: 19.9 s\n"
     ]
    }
   ],
   "source": [
    "# setup a model and run the chain once\n",
    "\n",
    "dm = new_model(data)\n",
    "dm.setup_model('p', rate_model='neg_binom')\n",
    "%time dm.fit(how='mcmc', iter=2_000, burn=1_000, thin=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [-----------------100%-----------------] 2000 of 2000 complete in 15.0 sec"
     ]
    }
   ],
   "source": [
    "# to run it more times, use the sample method of the dm.mcmc object\n",
    "# use the same iter/burn/thin settings for future convenience\n",
    "\n",
    "for i in range(4):\n",
    "    dm.mcmc.sample(iter=2_000, burn=1_000, thin=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0003183311847166"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate Gelman-Rubin statistic for all model variables\n",
    "R_hat = pm.gelman_rubin(dm.mcmc)\n",
    "\n",
    "# examine for gamma_p_100\n",
    "R_hat['gamma_p_100']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tue Jul 23 14:59:40 PDT 2019\r\n"
     ]
    }
   ],
   "source": [
    "!date"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dismod_mr",
   "language": "python",
   "name": "dismod_mr"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
